{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 82
    },
    "colab_type": "code",
    "id": "udLx0UyvnZTI",
    "outputId": "3e289321-8877-4ea5-9593-bd1260969e51"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tqdm\n",
    "import random\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "colab_type": "code",
    "id": "WkRQINROn6ew",
    "outputId": "8557ad9b-4035-4c89-93a1-f0f4377c88b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19634, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Labels</th>\n",
       "      <th>I_w_BLO_Weg</th>\n",
       "      <th>O_w_BLO_power</th>\n",
       "      <th>O_w_BLO_voltage</th>\n",
       "      <th>I_w_BHL_Weg</th>\n",
       "      <th>O_w_BHL_power</th>\n",
       "      <th>O_w_BHL_voltage</th>\n",
       "      <th>I_w_BHR_Weg</th>\n",
       "      <th>O_w_BHR_power</th>\n",
       "      <th>O_w_BHR_voltage</th>\n",
       "      <th>I_w_BRU_Weg</th>\n",
       "      <th>O_w_BRU_power</th>\n",
       "      <th>O_w_BRU_voltage</th>\n",
       "      <th>I_w_HR_Weg</th>\n",
       "      <th>O_w_HR_power</th>\n",
       "      <th>O_w_HR_voltage</th>\n",
       "      <th>I_w_HL_Weg</th>\n",
       "      <th>O_w_HL_power</th>\n",
       "      <th>O_w_HL_voltage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-874.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3434.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.043999</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-874.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3434.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.099998</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-874.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3434.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.143997</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-874.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3434.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.249001</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-874.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18471.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5764.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5008.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Timestamp  Labels  I_w_BLO_Weg  O_w_BLO_power  O_w_BLO_voltage  \\\n",
       "0   0.000000       0          7.0            0.0              0.0   \n",
       "1   0.043999       0          7.0            0.0              0.0   \n",
       "2   0.099998       0          7.0            0.0              0.0   \n",
       "3   0.143997       0          7.0            0.0              0.0   \n",
       "4   0.249001       0          7.0            0.0              0.0   \n",
       "\n",
       "   I_w_BHL_Weg  O_w_BHL_power  O_w_BHL_voltage  I_w_BHR_Weg  O_w_BHR_power  \\\n",
       "0       -547.0            0.0              0.0       -874.0           12.0   \n",
       "1       -547.0            0.0              0.0       -874.0           12.0   \n",
       "2       -547.0            0.0              0.0       -874.0           12.0   \n",
       "3       -547.0            0.0              0.0       -874.0           12.0   \n",
       "4       -547.0            0.0              0.0       -874.0            7.0   \n",
       "\n",
       "   O_w_BHR_voltage  I_w_BRU_Weg  O_w_BRU_power  O_w_BRU_voltage  I_w_HR_Weg  \\\n",
       "0              1.0        -32.0            0.0              0.0         0.0   \n",
       "1              1.0        -32.0            0.0              0.0         0.0   \n",
       "2              1.0        -32.0            0.0              0.0         0.0   \n",
       "3              1.0        -32.0            0.0              0.0         0.0   \n",
       "4              1.0         22.0        18471.0             47.0         0.0   \n",
       "\n",
       "   O_w_HR_power  O_w_HR_voltage  I_w_HL_Weg  O_w_HL_power  O_w_HL_voltage  \n",
       "0        2560.0            24.0         0.0        3434.0            24.0  \n",
       "1        2560.0            24.0         0.0        3434.0            24.0  \n",
       "2        2560.0            24.0         0.0        3434.0            24.0  \n",
       "3        2560.0            24.0         0.0        3434.0            24.0  \n",
       "4        5764.0            26.0         0.0        5008.0            24.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ DATA ###\n",
    "\n",
    "df = pd.read_csv('HRSS_anomalous_optimized.csv')\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pNeia09xoDwo"
   },
   "outputs": [],
   "source": [
    "features = ['I_w_BLO_Weg', 'O_w_BLO_power', 'O_w_BLO_voltage', 'I_w_BHL_Weg', \n",
    "            'O_w_BHL_power', 'O_w_BHL_voltage', 'I_w_BHR_Weg', 'O_w_BHR_power', \n",
    "            'O_w_BHR_voltage', 'I_w_BRU_Weg', 'O_w_BRU_power', 'O_w_BRU_voltage', \n",
    "            'I_w_HR_Weg', 'O_w_HR_power', 'O_w_HR_voltage', 'I_w_HL_Weg', \n",
    "            'O_w_HL_power', 'O_w_HL_voltage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "aP5wec9ToD5x",
    "outputId": "e34cd681-7549-4530-e2ea-10989c3f938e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15117\n",
       "1     4517\n",
       "Name: Labels, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### LABEL DISTRIBUTION ###\n",
    "\n",
    "df.Labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "pbFNhUNooDzx",
    "outputId": "75439c31-a67d-4190-cb90-084955a1e59b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Failure')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K9CsOJDu3wWAwGAyGywZNTrSlqhkMBsPlgNE80fPE7xuJ6OXOuaPOuZ8cq639Byut1B8r1jLeFexS+4M8516P1X6BNE4HbAz3/PsZAt1n2A/PtL59/Nh9T9OJC9P1mBu71oe0OTQJ7Nzbuxngt8vvR/0u1mDeDIaD9Cx7jNj8WYc1ZlAM+Dzi2OzHPe/gYUx2bpT4/dKx2j6wOHCLzdhYg/HSvLO1ea/r0o9dxtqM/4BYeKYVn2+Q8WC8cWpCyQP4TlTYo+duvfMHjp2jd3zuK/SeL99Nv/sTX7tr7e46Bm5bZOfu1ZY/mGsTEa3v972u/VpT7Mn8XOd3xO1vY/bZIl/2EsaAMQpsUu8qjOndYEjEOn5Dyj71+v471wy9hkTvp/0be9OenWlfj+9xflp5VC/OisT2NP0ZH9O8oGPnJkwXhuqDo2lekidHO1upBqTqfsOeZzAYDIahYUq0wWAwGAwGIiKaZPOw5ANS7/i1772LfuE/3UFlOa4HK4ybEYsZDAbD5YGDsUsaDAaDwWBIRghL3t3c3vHwhYdP7ko701wI527hf3vbTfSBu54Uz3no+Hk6cX46WN8MBoPBMDwOxi5pMBgMBoMhGXWppgPiid4taMatKD3lpac/u+moeK9//d676Gfeccug/TMYDAbDsLBd0mAwGAwGAxERTeYe1UMWlrwSNB782XxsNzZwLvPpS9kwHTMYDAbDKDAl2mAwGAwGAxERzepSTUZatQpCOPemQCAWvNXbQi1pf2AZuQ0Gg+FgwZRog8FgMBgMREQ0qUs1mSd6FQRiMQlB0d7e4EWvrDAl2mAwGPYDTIkeFdrNUDjPrNIYo46RFbTnsa5jkdKv2LVDP+eq91vXce5A/A7X9Rn69ivheZLXqyHmD3+P6Vie6H2/l8n9DwqyeE4wUGzxolcY/yH6tHz6fn8HhnGw5vNipXm75s8yNuwb31WYEj0obPIaDAaDYf8ieKItJ1qLat/XKL/hnC0hnDuMvwln+wGXm8znaf89837rr2E/wdbp0WD5ZLsGIQdtlOsMBkMHl+O3NOIzr7I2DbyO1fWOBUUPg7t2yL6ie1XHhxGhcb9VnmhFOHedNy14q1fpl8Fg2CXsiUxpa8BewpRog8FgMAwLVpiwDX/dERS9Q9vmia6gm7MhVFs8R6Eg1+OvUqINBoPBsFewVdpgMBgMBgMRNWWY9kOd6EeOX6AvfPnkXneDiJTh3HNFW2LnrvOm98H4GwwGw+UMW6UNBoPBYFhznLo4pVu+cmr0doIStx9iBn7q7TfT733sgb3uBhE1ucziOXWJK170CudI5GMGg8Fg2HvYKm0wGAwGw5rj5999O/3+xx+k89N81HY0ub2XG548c4lue0w2YKzkiZZyohUM3gaDwWDYe9gqbTAYDAbDmuPLxy8SEdHGyC7iWbE7SvTT5yb08PHzu9JWKv7Xt95Ev/vRB8VzViIW2xLCuc0TbTAYDPsCtkobDAaDwbDmCMqtGznQerU6xf3xE2/+Ar32PXftSlupmKygIMvn4HDu6T7KSTcYDIbLGbZKGwwGg2F/wFvNTwmPn75EJy9Mk+4xhCf69MUZ/daH7qVj5ybJ90qB38X5oqsTHcK5pTrRWNE2GAwGw97DVukDjVUFiDEEjl0QYqCgtAaC974V/vv22zM/S5f4+M+GOZgxWaexWurLGvVtX2CV8Vo+9+f+7Ev0E3/8RfaK89OM/tOtRykTFOWZgiCrap7v65/f+jjd8+Q5etfnH9XdaxWo5nt1Tl72WId6QlXiKuREb/Hlw1YrcbVX35c0rmv+ze/VerkW63TPPqxF3yXo14TqR7/4f6/7pZyfgLV/F5cXTIk2DIt9+YF3+rwvn+EgI+F9rOO7XMc+7TrWZAzW5l0MF6JdCM/0xo89SO+7/Un60N1Psf2IhyV3+sfWAV+8x+p5ve2+4zHxXj4reHVlDDP2q4Rzb0klrnqPXV+syzdgWBlrs34ZesHe376HKdGG/Q9RoDMYDDpI39FltNkf4PXk7KWMiIg2hGecDcDOPQvkWL1Ckocb/91kGl8lnHtTGP9ptsrYHdy5ajAYDOsOU6INBoPBsD9wgBXc3UBQ0I6I4cTpxGJNSDLfzm5A54lOhyevUtiDgUKaxg2Dt4lnBxEPHDtH3/7rHx29VJ3BYBgftkobDAaDwXAZILBMH9rGObkpCMrizvbeGj12yxOdl14VmakK+c5wyLdh/+LdNx+lJ89M6J4nz+51VwwGQyJMiTYYDAaDYZ+jLLEWN6uVaH7rH8J7O00K5x4OGrKvISARtbWxSi1pU6EPJrKi+k51xHEGg2GdYV+xwWAwGAz7HJrSVEHZk8onDZMTHRSFPQ7n3qWa11pG81Xypg0HE+Eb3Otvw2AwpMOUaIPBYDAY9jlUJZYCsZWw8w+heNYM05eJJ1pbW1tjoBgiJ92wvmiUaBO/DYb9DvuKDQaDwWDY59AoX5qw4xAVnsLHXodzj6goaMLXNWNy5mJGv/q+u+mxkxd792XIcO7JLin+hr1BmCubGxawbzDsd5gSvVvwkULv8RPHaHyEew6BHv2KjZ2G0WXMeny7VutviHbWdS6sGy6ncToAz7of620O3GeN8rW6Z7ZfH4PHdSNBwjh2bkJvvfHLlBVxRVjj/dWMyYfveYoeOX6B3vrZhxcPrPB+Qvg6wiq1pHXY7XmvbG/Quc3caz9+89TkRO8u1mWsYv1Yl74NhD2TBw/YOO4TmBK9r2AfyWpYh/HS9GEd+rl/8KG7nkryGi3jII6/Z35e9do9wl4LyHvVfkK7QfnaEhxcWckpcVW7pbL9zz18gu54/EznFs21Q+T1/ur77qFP3f8Mff6Rk9HjuvD1RiFdfjI/PyeEnsdzVL1iTGaMor90niYnOgvEYp7W4ls0DApt1ML+gM3PRTCLr0/Zj1GTFtGwlzAlegzYpN5daMfb3suBwKvfdjP90Bs/vdfdOGAY+tvg7jfGN7iO33WsT5G/rbAmefCcMA/ZOZpmsgA3K0ryHvfp9R++n37pL+4Q7iMri7lCkQgK8DZT6mmqUFxVivacjZxjLNd4DoMnWvuOJL18NQPEOs59g4SDpUQbDJc3TIk2GAz7BsU8D/LsJN/jnhhGx64avYZoa69rIhdE5GhbyEOeAgF+OkB5q+o+oB2FotgwfMefZxiSLtcYH7h2VlD4YVtK7zlSxg37F3sdZGMwGIaDKdEGg2HfwJhrDZcjVJ7bTFYGiYgyoHhqWaYRUI7wKrnBnBK9Ss1l8Zz5Mx/m2lEYFrRlwTTrlxGLGQwGw/6AKdEGg2HfYKJQFAyGgwadNzS9dM5QJaFQqLWKSbz2EMdzlXUKqT7km2tnlbEf4rygkJvD0mAYHg8eO0eXMotkMwwDk0QNBsO+waW5UMx5jQ46Hnj6HH3y/mf2uhuGXYZGuQ0K43ZCbebJAIRgRNgzq3meoLxylYAG80SDclwaRVxDGLZqfwz7E2cuZnRpNt47vDjL6Y0ffcByq3vAe0/f+/pP0W996L697orhgGA0SdQ592bn3DHn3J2tv/2mc+5e59ztzrk/d85dM1b7lws+9/AJeulr3kcnzk/3uisGw+gIoZWHt+Neo4OOP/rkwyKhE8IdR8/Q++94csAeGXYDqyiMLLGYAhplUMNWPURONDpntoJhQWwnkz34qtJhyrJFmtDwIZjNDXuHb/mVD9H3/c4nR7v/Zx88Qa//8P109xNnR2tDQlaUajb6dUP4tu5+4twe98RwUDCmO+ctRPTKzt8+TETf7L3/G0R0PxH9wojtXxb4w088REREtx89A840GPY/gnfqcg3nnmQFsVWKFPjh3/sM/fQ7bhmuQ4ZdgcY7WbNZp4RzKxQ4DVs16q/meZCSrGLnXkFZ53Ovd9cTrc2vNoyDN33qIfrAnU8l3ePoqUsD9WYZk/lc26tw///3Uw/Rb36wvyf3xIUpPXLi/IA90iN8fxzjv8GwKkaTRL33nyKik52/fch7H5IRPkdELxmr/bWAloZRPA8RtMjkK5cFdpXucuy29lEmXM9xv+PoGZWHKAbkNaqQMoaxazt/S55v/es2r3eoZ/91bHho2+vbr6HnmIxFxSp+fc0XsOSJjpzPdGHRg8y0o/JWy8c13l2Ui6xSkFeoJc2FwWvukS0QqcUf3ns/WAg6amu485fx6MkL9PoP30dluY/2Kgmdyfpvrr+XfurtN7PH9xp7TTz3xOkJnbmU9b7+n7/7dvrVv7xnxatWeQf8uc23vl8i2frMvfWarwcde6l5/S9E9H7uoHPu1c65m5xzN+W5kQBwqK3ol2l4K8LDx8/T8XUNdb8M61afuZTRj/3BDfTeLz3R6/rGE315zve9FqDGxeX3PWixUm5vSjj3CizgKVB5d0FfEAO4tp2QW+qY9Vjj8Z7lBTknL+mZMoRkbEPZg8+cp5986xfpiTP9vKV3PXGW3nfHUzU/xaqYZAU9cXo8Ty3CjQ+f2LNQ6CGQOj+896qUDA7aqIt1REMiqNxrDpiMdvLCjJ7s+d0b4tgTJdo594tElBPRO7hzvPdv8t6/wnv/iq2trd3rXAraC5Py43vw2Dn6l39xB+XMBvv+O5+kn37HzewGPFF55njkZXmgw8f+4OMP0btvfmyvuxHHmlm4Q38eO3mR7n96nJyh89OcitL3Jl7ReaL3Dt773l52DZAAlSog9ccQwsZu9nuA/o4qYC3eW37v1bnh29jeTiAWU+XsKjzRYHxDX13CO1flKg8QPg3b8Z5mRUmHtjaEp/aqMPiy1J2XghsfOk5ERPc91U+RTCW0eufnv0L/4A9uoJJZp97x+Ufpp952c/SYBk+cvkSnLszY42/4yP307z7zMHMUj/17bnucHnpmHo68B2ttqhHrX/zH2+lrf+H6fhd7rzJejYqVx7wdyRXCufk18q4nzjRrQqetc5OM/rs//Cw9/Ey/cHRPnm4/eoo/7j09cvwCe/zJs5fo5959W2+Z/Q0fuZ9+8Hc/3etaQxy7Lok65/4hEf19Ivof/N5Ie7uKS7OC/vJ23uv2z/70S/SRu5+mx05ejB7/rQ/dR7O8ZIWb1HDun377zfQNv8QGBCwhK8o9EtIFCMLspawQhZJTF2f72rIaw9FTF+n6BPKo173/Xvr5d9/OHv/4vcfotsdO97q3hlhHQiA04XKiPXn6jQ/cQw8e25ucq/fd8SR92699pLci/UeffIje/JlH2ONIKfihN36GvvG1H+jVtmF90RacPSPoh7mhdbLEMGQ5J3QPpGgjaATJIcKnVYp4QXQIRMdolE/N+Jelp19+z5304LFz1McYFPbDrZ4RC6lK/vHzUzo/zVl19davnKabHuUVjf/p33+eXvWmz7HHv+N1H6PveN3H2OOTrEzSff+PP7mNvv93PhU9NoRs9J9uOUovfc37BJkv7fv705uOJl2P0jkeP30paf992+ceFffAj997jD5+37Fe90bROicvzOiH/5/P0Cfvj9//o/cco5sePUVv+MgDvdr/6D3H6NVvu4XuOBqXn258+AR99299gtUH3nLDl+nDdx+j25nrz09zuln4diZZcdmSso6FXVWinXOvJKJ/QUQ/4r2Pz5IDhg/e9RT97DtvZUOnau8BoxQEcpVNps5HbdHv6RW550m9x3GSFfS3fu0j9P5E0o3dhCS4FKWnf/WeO5MUzrGQkm/2Q2/8DP2MQB71/juepNcLJR7OTjJR2f1Hb/ki/djv39Crb0gAOH5+Sj/2+zewIUfIE/3MuSn98Q2P0qv/w03R48iSfPvR0/TS17yv8TR0UJaeHj3BW4ofO3mJzk7y3t6C99/xJH303qfZ4+i+dz959oCHfF+e2K3SSKoc4l0K50YISrSkC+rqUctr7SzTsJEX0JCN2qnug8f24ePn6a03Pko//fb4Gn/01EX6ptd+gB5goomCot63FBoyBpy8MKNbv8IL8qlGi08/cJxufPiEeI4Uai4ZzZESHI7nzP48RBTBv/3AvURUGfhjQAbaSVaw+5cGT565JHpDkfHqO1/3Mfre1/dnJ/9Xf3En/cpf3s0ef/vnH6W3fvbLve6NiMXOXsqo9EQXmUi5MPZHGEW0KEv6xH3HWPnt0ROV2nOaySl/5lyVeqeriecAACAASURBVHh2Ej8evj3OYPez77yFfuYdN9O5afz6SVaubRTffsWYJa7eRUQ3EtHLnXNHnXM/SUS/R0RXEdGHnXO3Oef+aKz21wXnp1U+d85sPMGqt73BkJqEfC3G4ryb5TDOTXI6fTHb03ymVZAXJRWCMjrJCro0K+jsZL1y7h87eZG+7l9eT39x6+O9rkekHx+86yn6j7fw906ZU957+o0P3MvmnKF7/9lNR+m2x07TW5hNclbISnQQYjijE7Ikv+e2alw+88Dx6PE3fOR++ntv+BQ9dXYSPZ6qHGAB82BFTRh0GMKjyu1Bi/fA82sCiLg0mNTG3963qPdGyaOqMgqAnGdt/vYhEEavyYlehXGcG/+jpy7RxVlBjzP7dAaU6Euzgu4VQr2REv3jb7qRfuadvBEXMrePmA5DJOfSc8pxAFKSkZeWC2FfuEcmK0roO/8n77iFvue3P9k75PfX3ncP/bM/vY09vtfpf5poDQ7IaYXGNhhnjuzE3809T52j3/7w/XT74/FqObNSri6C1qsw/7i1JlTp4abZNDdP9NAYk537Vd77F3rvt733L/He/3vv/dd777/ae/+t838/NVb76wIoFGdBEIhLE2jNHXvDWWhrnwnwaENc13qcdz1RLYSch/zGh6ra4FU43+pAnsqUcZnmJf3BJx6ijzHeVDRfwwZ9CDDlcgJGballNplgYb6C2QTrWrvMJvfpByvlOhjHukj1AqOxNy/z5QlViSsogA0U/gwE0bY3j1uBESO2BuEe3N5ZnTNATrQqnLscJJxbxyYur1HIUxkEcc4b90/fdQu98g2fZu+DPOoPP8N7MYmGqSHeF6X3ojEDzQVsABjiG6za2OSI7kAfQqgzl/aBcOZSJnKWTBNz4lOREgmDwrnR2NYGDka+CN8MZ7DM2AoKuvbD/OGuD+1zc2eSlZdtedCxYKM5MuBHObLnakisq9LJIdunXr2wUHMWw+At/eKX+ZA5CWN6AlIFpJDzzJabCRwAzCaGwp2CJZkb2yBE8ZvUXIHg0itSv2cw9us6Zw3jYohw7vY9OOOsLs9YFkRzhbdNQ/CDEPoq0XlpuAm0+wTzyVd9yUo6DDzRQ7GJo5QWNFcysMZ96v7KUMi9Ri3LOIe9jLbhCFy1bc+AAUOrBElAhlJ0PPgOuOhFBLhH77EhV/JEF97T977+E2z1j5o3gpUv0NjP5QfWiA8iGUp53YPfbiGnd9brHfPqZ7mFcw8NG82RAZWKxAUJhbY8+Mx5+tDdw+Qwj8k6PAZmYEEbIrdvDCCyuPAe+i6GcJNM8kTrBfno8UwWUlCoWx3uBMaOC8eqPeGMQFx70HoKsLc+dlrMwR+C5OhAY91IDXcJQ4Rza75rFL3Tbof7RjXtzGphtn889ype25RzgtLJCa5E1V6DPNG5IlRbxzgeDIn9wn2DIM8pEkGm4ELtU9m5kRyRmk4kAea/K9ff3gaMAWprj21IxYbuPVaiheefZAU9fmpCv/jnd0SPN2uXzDHEYQrkA0RSm9ekfv3SM5GSjqbXNC/gOmVYDaZEjwxo2Rp1QfT069ffQ+/8/FfEc7QYTIDvbnSpgjFzfQZz3RTPswdCO/JENxv5/PiKfUwRYuKEGcslJLjSNVolm/PqIANDBnKm602UswSDcKk6lJXjMACb8O997AH6o08+VP0SeW/iu/GeeTfrpFiuU1/2I5jvRlV6qhTuoFvvkFeWqJmjjB1qQRFZFBXb64QcdijDL9yj/zkVoOI0XxM2HD+3Z7mCWGwFA4WExWiZ5XuiuZLVa3Q/ZIVfXN+l/SdyTGtI7QONJ1AqqwYN70CJbr6N2HFPeUuJ7ytapMlhXKOtbzM6f9Dx3YMU0SFHovm671wqA5aNgvzR0xMNeBxwKka6083CuYeFjebIwHVdd6kjA2BdPbccUPjcunrWkaKIFE18//4hbamheNjSG8Kt5WfnNsEchHOjcPAZ9DTI4VJjpmfkpUyUZzi4GCKcW+WJXiFvemdTjuZI7Yu2H6nnnJ/JxJKq5ylwmKSKnVsTzg2jZeT+5okM0hpDiwRsSO2/hkqs3ERYyamNOyBdB5F+ISNvCsYnXltfT7QnL7aPItGgEV0ZzcNG4SiVaG6l0IZz98UsL9ln309wzr3SOXefc+5B59xrIsf/inPu4865W51ztzvnfnCsvuz/0VxzrH/4pd4erdvcemzQKRStAjSeaE8uYpneW0UFeaKbcO758RXHL8UTEJ8DTfuppGVLXvYOwrNzJd2W2SsX3+UM3B/mGw6ZntF5hrwoxXDa1A10d+u7M3NypG99P+D6O5+kX/hzrv66PC4qTzSYezNFOPGs9LQVUyBa722aV3V2t5mQSFmQrK7RKKaojvQ0L8A5jqYZrkeNjmuNAoj1dmhisf4hxWnrCMorXkDke09JWUFrGPTCg3cAjajK/YELtR9CAV0cn+HXdDESzfukfSh1D0JtI2JRxMWgjZTrayRZNmAt/o7mb+oWPs1xFYF1h3Nuk4h+n4h+gIi+iYhe5Zz7ps5pv0REf+q9/5tE9ONE9Adj9Wd/j+Y+wLp6O/tgbZmBGcEch2at57vReqL7LoYpecvpSrLOU91bQNSGc3PheIk5zylzCglYqQa5vc5lu9zxGx+4l46dnfa6VvPuURSELlTYQ7KvaR2y2l9RQH3h6qyucg9PfpA1XjVuGk/0QCXGFr2hMSV15JDQVE90wh6CPMnQE630NHLvMsxtRCzGXb+SAYLB2BGB0vxJ7T8y4MCcdvD+89oTLUeicZFseiO/XGKTA3r+sZ1uB8QT/W1E9KD3/mHv/YyI/oSIfrRzjieiq+c/P5uI4kxzA2Dfj+a6Y/090Xqsq9LJAQkL6xqejnOi56VdJLpYxf1jKEs5XCo1FE+rhLKWZFRCpBzG08yNLFR0U8pvoGdLVDygEUzyEu+nvJMDCI0xFnqiFeHEeVGyAmbdDkipSA/ndoMo4kXpqfRpDOBEuvDlWd4Qi/Gs1sPmRPeNltGElUtIDQdPqQ6hLUHEbY1aJabv/oFKIKWOPdHYPDryPjH23EHfvdoTDb4NnvMEpcTI4f4o+hE9/9hOt1l+IHKiX0xEj7V+Pzr/Wxv/NxH9j865o0R0PRH907E6s+9Hc92xropaH+w3gwBUotf0eZAiGTY5LqQZ3l9YqFO9oamkMeHZNhniLiRAhE0e5TSzQlByTnNCeTCYi5YmgOw3I5ihwRD5v5qQ8LOTHHuiQUikqkzWAMYoXJO3+l70QmP8+9Io9LO8gJFBGmV8V8K5U3OiRy5xJdXlVhthOeInJTs3qs4AOTdY4qn97YlG/YeeZGBIQmuH1tPLRwqE+6d5otn2wfzaa5l0muN69muCLefcTa1/r24di7287sC/ioje4r1/CRH9IBG9zTk3ir67NcZNDQ3WWXAtVtwM11Xp5LBfw7lrTzTYqPtimpd0FXcMKrlyjVh4faonG+Z9ykJQ2KRZ4phEASXlG8FhmImkbQfBoHeZ5lQPQiymEOCPn5/SNN8h2sHt9I3WINKE9DbPwq3iWOAuichVa1XCkqlToocJ59YY8Ra9oSsy/NMAIbmJiiDKeZbeqzrclvMEK3OiEfEUyrnliDHXvcRVUXpRUYU5v+D5EHFhKrFWVo9/P9kJRWulkq6h8dmVcO794YnOvfevYI4dJaKvbv3+EloO1/5JInolEZH3/kbn3GEiei4RHRu6o/tiNPcz1lnxXNUivdelDVYFqtm3ru+m9vQwxD0puemeLZMU2lYSawBLPXv9QDnVnMVbLnGRbmlGSLkeb+BgA4ZEevvr+zU0WIW1mYPGQ3xkZ5OuPrJNRHxYcrhPX+WWKD3tgwgriyh/lagiSkJAzxMYg7XEYlK96bA+bjGROETN2GFv2zhIDemVw7XToilQqDvyogfyPe5d4uoNoP0BPNFjctMgJRcpibhEU6InWump7hulkbouacPN+7afgpCqt0+UaAlfJKKXOee+1jm3QxVx2Hs753yFiL6HiMg5941EdJiInhmjM/t+NNcd66qoEa0elrXOzxIDWrDXlfQtCAqOCzlK6HdyuHbtBemrpCJLcJqlOEc5UchblzgnUuYULlGUZqBYW2JAA4Tm3cEap8qw5OdddUi+zwDK+iqe6L7tBIFbEho1XllkvAq519gT7enQ1obIBY6Ij6r+oDV43H0tNRxcNuIOY2Tl3jmWCWRPMgzn7lbOWGp/gHDuhPeLibvSwqlTlUz4TQNPbsOJ0rO6RqL8gsYH2ezG3KOD7Ne3NOq6wHufE9HPEtEHiegeqli473LO/Ypz7kfmp/1fRPSPnXNfIqJ3EdFP+JHKk1g498hYZ+/tqgv6Oj9LDMhqua5GgTG9pWiT0iq5/S29u7OJcd4eHbt4/00mLZwbeQEsJ/pyBXp3dYSJEIatMRDNck+HjsjzH4Y0DuCJVhkN8oK2heNhfwuhuTEZSqMQIuNVBhiB2/1BivY0L2nDAW81SkkZcV8rvU8KBw/zdIcZhlRPIDIw6Nm5ZSMxigJg2bkTDRDtNvogVUlG12NP9N6Gc6fukboUkv4Yc48O74ZLE9xP8N5fTxVhWPtvr239fDcRfedu9GV/myT2AZqPrr04CQvVCsYSFHqD2lsOyxohX0N6nl6Gocg1ILS3qgO9fM5wOaKa59A/K79QV/dIqaOJasUuCa+d+8Fw7tgm2rqHPId8cgmt1E06tQyUZGgK72a5Lnm4doVni7xn6IncE0/0urN6r3v/KkTfXWsOZIWHy6nOE112atwu33QYJXqV75Qh/FJ6req1KqpES+1Uv8M0C8DD0G4rCPfcq5pkBVTG65x0F7/TmN854mUgInLO82tcH+LJ1ntbXOMizw5ymrXs3KwnGlZ3kEskpnrxvff9op3mY5hqiE11TMSvb/rUX0n18+MgnBvJJ33lDx/aL9m5r4Hm263vv6L8jKq+GPrBlOiRkRLeiRQiicVSg1VJLtY1/JnD8oaxaLnfc88cQ5KU0i9EXJG6CWJ2UiQkpeXtJlu6R57DYo1TKMClEUONne9tkHHqwoz+9V/eRU+fnRBPu9WF7rwh6pNPM08etKepd4znaXr+9hA54GGtOySwjWs8g9q6y5pw7sqDKXuZUbjlmLXqEZCSUwCZJXWNCus/56iHxGKIVwLsb7hOdDDcx5GVae8mn6cO9EVqzi6SGTFPQRrZq9ZTPhgnSkdGw0r2eKkOUThH+j1EjiI09ION5siQPgpU1xXmnyQqBBqr8kJ7+0wIz4pcPL7nz8MIHCneUHRtajh3anmVXsfVnuy9zYPPi1I0YkBisEQPH84n1z47Ny+HHLt19wCv3r8P3fMUfeXERfrAXU8O3ptVuAL6sWbPPVV5wRIa1vcBkTJIUCaS9i4cbRNQj4mTI5GknOgh6lGjENJ2f9A506xVgoYZxvueOiffY9V9bQWPFmRnTgyX1e4/PPuyvD9pI5W4IgC1p5kxzEBm9AFLIPVJ8dQSc/HH9zZSTF0nuifx6dL7W4rE62vEb+4jeapHdbqZJ3oUmBI9MtJINNLyg4pEJX3V9vYMzI6HlZb19KynhOOllpDQepJXCpdauH9azlGqEj3mHE4VAKCXPVEJT3n2M5cy+qfvuo3ueuKM7gK2FNV+KFHVr4+TrHo/25urCylZUdK7bz5K5yZZ9LiatV44Rxdm7UXPraovCkEQRVGhNbAoZYMVka5OtGZMYG36EDauKHGl8fIf2t4Q3+M1V7QzwZfn6phrHNpTcSTQMJ5oxJ6905PYCzHPI0/z2PsTVNITPc0o/x9GQ62SkhS7ft7+FhNqMBw7d890g8T3iyCm6gHDrpY5fb8Ti60bbDRHhqQ0pJKr4A8aLbirKtHrqXRySN3Q9wopnmhsSUbRDWCTVYazsfdPFPCgognC5cb0RKd6klPLeyHlBYVCSjh9aUZ5UdKpi3El7y9ue5y+8MiJ1W98gBDGn8vHlHD/0+fo/Xc+SbcfjRsphigJpWK8LkqYk5taK5Uo3diGFGiiZWIx6RwJ2Lglh5A2bXmoRE8yrGjDdSRhjUtVwlKVRG2t+8Og+gJf4mpcJXYKlPDUElGpnB4oSgTKD2h8Eo3kE5BzjkjtwvzklPDUqgCphnIEqX1oAFHOLbROGVaDKdEjovBy4XqsJK8iaMQIYNKsxkvtranSySEDG1pqTvlYGNMTjWtn68ip+pZXSZ1DOLRyPB6B1FDF1HxtrYCKa5j28ZTKbX/orqfohocucyV6/n5ROHT02kThTfNd4cilkooSK3rYWKTJM057Ho2intXvo384t1eQHepzorGBYpoXOOQ7wVjXjk6LvSVILDVyHVzoWMhlT3R4V2yJq5Gj/1I5OzA5ZNr+ihwr2vfLRQ6j9oPxi1VygxGEeb/JToLRozv7yxd1hQXu3kieVxrpzRM9LGw0RwS02q1gdYyFcmg3977Hl9rbd57ocXPKx0JaTjQI+Un0hs5GtvRK8N4r2Dvl50eboAQsQKUKmGjsdZ5mPl8wCCirL/upHqiDgs89coLe+NEHosdSPNFo7sCcaFVdZZ0AmkwshrzIgDtA00btcRLqKdfh3JInGoyJppxTPW4Kdm50zjSvPNGOTYdI40ZIjc5q702xkUv2RINw2/BsbCRUYrizdn9E4d4csBI9zP7Mtp/oWNEqqXyd7rlhi80plyMNcP/GN0ZKkOaPJhw7GCdiRooZWIuQ7IfkA0M/mBI9IhYWtMj8Tg9tGsaqqMV+80QjpWNdc7xTlPvUkB+tkMMSr2iFkB6kKJr3pS/7tjrSa1injX2yJxqEyn32weP0L//8jqgggOfVehqkhsZDx87TvU+fjR4L48sJiBJE4SvU1hUURs3aDOsdz4U0zgsUoC351Pc4kT7iRMrfVnmioUGgamdTUGoRI3B9Xq7z8sNw+oR1JDXnFdZZRntuorGoDudmPdFpz5dOvJkaTdRcH3ecpI0vjjQYJuedm+dhfLlonZpzBeS88/1LDNdPdBRJ44MM+P3KgzWYtedGpKkQhWee6GFhozki0vNfUvMzdJ6HbUE4W6W9dUNqaM9eobF292DfTBUSkBAycjhc6rWojJSE9njHRj5dgEmbj1oPFM9MGqII4sd/64P30dNnJ/TM+enSMelb8iBt5SBBMtIE5TKmtCGjkRjGN68BHbw7sVup8p2VYcC4xFJqyOsASnQph+0S6XKiteHLkmEhnIPGTRvOnZwTLXhLUyOVUpWwhbkTmchaJYeTWXA0kJ83zfFKpDo25OPIyJvuiZaV8FTHCpRpQbh9GH9uDwqKXt+c9tSUKa2jiFvO6/kVOYY4iHAofhqx2NRyokeBKdEjAuefzr16bOhSqudKKSAoP6p1VTrZUlGDkFztsnLgvRjqmFoWLd0THcitiGJjMwRzL2pbwqpkeW0sWor5Z+O+V5R+oQ3lW5rO8z9omVFRjdNlVPefFrwAI/W9HYa2d+h0YKQO1UJc5P5BgIx5jOPfRXOPaS38xuYdFn5UxGKtdxibw9r9IJ1AD3/HMMoKsDATNWuBrGijPMNwD8dOqVm+OG7c+pCVuAb0JMNlsGoGaXYN5sdulmi4R+8Wrb+pMg27RobjvTzBse+QOZ5KrJWq5CWnFI0czo2Iwebjy5cQ466v2s1y+f0PV8Fi9f2jKEuxTno7NSR2VvTba90PR3no5p60HhpWh43miEA5aOme6MTQrBU/Ko0HYZ2gDT3aMwVACBHkkEr8Eq5nLfEDsady0BJ7xfqnsRJrUxRij58auQBDCROVC+iJBuVBsBWev14KJRsyQuXj9x2jZ85NBrvf0JDmV50PGPF8wigF4fi0Vhj59ULDOo890dX819QyFu+jXHe5eUqkz/2UxiQrStpwcii2Nrx5Ryhbpi9x5Uf3RBelHBWCeRUSvWGpkUqpMhHqP6jekMzePDIvxtjh6pA3Qfl++9fpnleQYNYGyB5f8jIdIu5qt1+jtXakloxFx+G3B96N3shuat+QsNEcBY6I3AAlbbrHFz8iuGArw721H9XaeqIZpIbPDgOhD/VKr9fiUzfpLJPa8lhIUG6CUXs/IAZLzRkiwmHHzJH5tSsYCHpwHDSkbIr7R7Ba//zSz2L/nKOpJHwL72YoroTz04ze/rlH6XUfuI9W8wT0qNkVhKMVLWiLgsxiuw1fQMwTXZAX+im9m4ZMaQtevwkUU6m8mcYTXQmiQJDPStoO9xC+k52tDVVUR7yvnoicWJM7K/28DQ4eKg1B6ZT2yLw+Z5P4uegpyxV1orMu+VjEmChFhSQqSQvRNFFD4zC8D33fu9aQyAErIqt6ghfXWVziKU0mSR2fVGKu5esXn2dSG8j6KtFISfbs3GHvP5/ImlSvpfbbnuDEVAcYzp3oFNP2zzzRw8JGc0SMXRNQs+CIxwsseK3S3p6B8TSgDWG/edaJFoWEmFKIw7UTLfEJczaVmAttEkSypTrVi59MLBZIVZgczbHrTKPrpVSBLKHevRYXZ3l1vzVlzScimgmeLNlL3X8vqJVOiVisPkcm0arzqoVwbskTnRWeEMH9tJDDlhGDb/scqR9ERIe2HGtvyQYg6dIwbzd500Oxc69Sp7tryEGCeGp0W2JO6shrGC4xlehJ3+uc6ESZMTVcG7Nfy+HaqdFeKcRiqG1k5E8lDk39NpASruW86FM9wsDDRnNEJHuiUVmTxPDRkHurjSreb+zcqUyNewFIQJQYfYC984mbfJISnaakEpGYT55qpU8P5ZNzNFOZXVONchKkDXyo76hmt67Hp4eHuTd0bUmeLOlYylqNct2rcwLhkqSYlmzpGKJGodQowBJmeSmyZof5InmRtfVupTHJyxJ6XXTeIwfLZHlywFtdUu5xvvkkK3qVoAtINsQNFN3G339cMlW0P8Fw4MQ9ACrxwBuK62SnGRHC++dSKVKJ41KZ+5NzuiWCxsEjQzv3H4jZnAMqt6fxVG84oi1TogeFjeaI0FrtOKAFM9nquILw6xWhSuuG1A1xL5DqScaegnHz8CUhKfneYL7COozJAmBaKGF4t/090amRK/3nuzQ2QxnXGkVwN5Xn1SB7OnQGpGi+v/Bd6jy3OFRvlnuRiEvDRK1KqchL2hEUwTo8XRDmtARRUvmqrPCYMVtZN1f08JdV+TGptnNdPkzob8jZROH0EnBOb+r+MnI0D1LCUsOZkSKSSEyFZLZZXrKhzkS754nmog+HqhPNXp8cXdnfSIEiM5OjDMDcQnMPrXnJnmhFdQDD6jAlekTABTl5wUzb8NBH30Yos7KfMAw79+4inX0zzVo5qic6gVwJ3ZtI9kJrrh9bAEQCOSaN2TtPtCS8DGWMCgKoROK01xDTBRJCAaW5E745SZGbZgU5J6fmzIpCVG7r2ssKZV3CrCjpsOg1D8YknvFa+y1JHm+N0IgE6/C+RWU9L8XjRETTDI9tWJtTxh/n5I7rbUuv/pC2f6WWeExR8lA4MFE1fsFIGDP6phqSMUOzjo2ePb5yznj3+rRIBdg/MVJoXAcCjAJM1AdSZbtZjiNzDKvDRnREjL2hDFUnWoN19NoiSBuGZsPbC6Sym6aGDEFv65g50YnlP5JJS0bmMAj35xwRo3uiEzzGcqjyMN9RUADEcNY9tuRJ3gRp/mIPmxQuP/fQC0zU0zlpleTDR2HWmpxoTb76DHiiwzxNKdk1q0tPycotIvLC4b3zdsRx8zDPsA4/V3j55fFP9OQm7g9oDUpOiUlc41I9qTPkLVRwF8jtywaXVMcK8oSn5u2mphSl79H9Q6ZTU9lSDTypUSDQSaBwQpgSPTxsREdEcrmH5PylebkAjuQBMcSs0Jd1g/deFJDW9XmGIo/iiT1GnpMJBFSpz45rlKblHA9FLMZen5gPlloyr2/bg3miQTj3jQ8fr8nH9grSHJWOIQ+hRjhHSmc4Hi3vQpXRUBLg65zoRE/0NC/pkJTvHMLTE3KvQ2SERLZWlZTCZafk48ETLZfS0palks4Lc0A8J7XsZbKRNs0wnxpunpqSI+1/4RuRjkvfKVp/vfeUFaU4Z/H7TTQ0JyrZqYpiqvwBlXwpnHtkAw+O0hiGU6X3/fPCSMVGgI3oiBgmh7G/8Bosb9uM6ytfQZEcVukc2JsUyzEcK5R7ZE9YKntqWGg3GC06OedL2KRRjdLUd6IlTXEu3geNEh29ti4RhRShVAEE9Y87rihhRWkeYymMr2lXmFuK76YJ811er+584jT9u08/Qn98wyPwPmOi7/xmy48prq1ZVWvlavn6SVZU+b+x734uvBPJCnLWUdZjpWTa3xBXamZWcLWOF+epGJ4Ow26DJ5pR1ufPjGpeo/DUkPK0s7nJevkzFYFZCOfGXmYxnHvpG158B2N7y+o1lnn3Y5MzpjxfUGK5viNPX1HKzPQaBdJ7QP6XnNPec3xCGSh2fZsfT9yjx44WY/dw6oZzx9a2cY3cOF1Ntxax989louBZLldNMPSDjeiISA2dSl1Qw4K3yVg+0YLUBlocdxNvvuER+nefeVg8J9Vi22BEpTlaTzY1XA8IKYk5TdI8SC4PkhrKBzaZS3CDH5e4K9WAkR5unuCJlupED2Rgkwi0Tl3MiIjo7GRvPdEieVhCqGfz3fIKNipxJSloTb4zf06t6ClCsTkU3lNeePkeCmIxTX7g9taGSKqeldhD3A7FjtdFnhuiRXbuEnp4gkFdZj6fK9FiyHeiJzpVpkglFkveA/oraYWXeV0gcVPq+jv/xiV25JSIFSKFJxp6mseN1sK8Hv3nV+m9WKaxV53mloyGv42CvJc4KcaNtNNE8hmx2PAYTYl2zr3ZOXfMOXdn62/XOec+7Jx7YP7/tWO1vw5ID+dOE+rDR+sYSWOlnGiFF+vCdHcE3BsePE43PnRCPEc7dn5Xy+hgaBgWJaQQbxDhTViac6lKHBLwoJU7mbhj3FDEVCt8KjNtkidaytnNClrWZrjvSlAEBU/0TDi2nqyuzQAAIABJREFUW/Atj24MYjg3zGXsjG9LeGs80ZwA5Giac97f+T2CR1Vksy5pc8Mx7MFVf9rrZmxGhBQati/O1XmbYm5oXtRjwCm3OITa06GtTXGNz4tSJLJrwrlBTrSylJYckj9XtMWQ71Qja5pxOXX/STUST2b9w5WRAgfZlROfLaxhojFsZE80rtgC5AeYM566B/ZX4lNLQKUaeIZQcsX7w+fDsltK+TxDHGOO6FuI6JWdv72GiD7qvX8ZEX10/vsBwuJHlFyXLlWpGJCdGy0gNzx0nP73P7mV7n/6jPqeYyJtQdwl8qKIdJhcxxMyNPbPmfPk04jDBjq+tRFn9Y0/e3NieqhZWihiqicDK8lIUUvJie7/3rWQPK51bWEhN3UV3PPkWfrtD98H30kbeSl7skRiMaWAHb9W57mVBKQ8x17mTMFmrfV0abzMsKa1SOZVwFDtWYGFxqzw4pzKNZ7oooTGnZli/IMCkuaJTlQkUnOOE+sgp5b9FEvQpebbJithunkv3yPx/cNoLpxTzoXDV+2PS3wqXY8MAL3mfmvBT/22Uvf/dPZv2dBq6IfRRtR7/ykiOtn5848S0VvnP7+ViH5srPbXAXvPzp22Ia7S1pceO01ERF8+cVF9z8EQDYtOy1/dK6RGL+CFvL81fWxSMo2XxDnemwbDnSHxV2K+eCLxiHT/wvvkEl5J7Ny7qERvRzyDYWw5fodV8W+uv4fufuIsPXL8gvqalJQWrBzgUHDJ2znJC7GsVO0JBSWhUM4cymfONLm/eUFbG462hLrK01xm+M4KLz4LUfU9w3DuUj4nKzxtbTo2z7A6R6GsB+Z55H2H53S/Ndc5Pi55I0p5SS4TCI5fEjzR3ntxD0kt75TKaRHGXopqgIbQZMdKWgmr9Giv/u/fkxwJhAw42KmVmE4Gy4OlpnOle7r5aCZDX+y2WeKrvPdPEhHN/3/+Lre/q0gNnUrNf0GWqSFzojVC2m4ipZ7xXiLZcAKtlf09Aemheon52LlcxgdbepEVflxPtCSgoJJrqBQPkfyNeu+hki+9XVmJHoYvoVGil99wLYAOtL40jNf6+6WsGSlhuJpnR57ouq6ycM4sXwxt9gIBGsegrmlnmmPCr0lWgNBzOe869AW1kxcyKVhWyjWvq3vgElcqYjHFnEwts5dspAV7AMw7TdiX0RqGQ82BgqjwwkrQpmJJhsDxPdH93y9SYqv25T0IK5L89SgSaBRP9ArHkfyA9nAY5YPkE004t3miB8fajqhz7tXOuZucczfl+d6SyfRFKolHOhMjyuHQC4WY9CB4S9bD0pW6Ge0VkktcJZKjiMI8WsSRlTxVyc4KmTxp5BqW2DDT3wuQGlWC7pEVsgCCEL7vmGI1VFRHUw95eVvSMDqvgibPeFkZ/PefeZje9YVH2T4gxIYZR5BIxGKV51Z6dEQaM1Osz3lZ5fZKAfN1GTLmPrUnGoQkI2EOlcnS1IDWlJ6qSg7JhgXpWcI57Xt45h0S6Zi3xTrRiSkdqbwJUFFNTomRIzKkNQz3HXmi03JetfKalD6AHSdp44uimaT3h5RYIrl/mj0oJRIuNd97bNK2sZnJZxk2UJgSPTx2e0Sfds69kIho/v8x7kTv/Zu896/w3r9ia2tr1zo4JFLDf8YOL0GbykptKZhddxOpYzckPnLP0/TgsXOqc1OjF5LzZqRNLNVKnuhFQZbU9E1qGCGJvX9CPjnykuRFKQpIbQ9OTBFGUHmiE6kEghAcC/NtFGy9ka4oS3rTpx+io6eWU0yCRyZGonX3E2fpgWPLYd56Rv9lpHjgphlWBpGAFBQAyetaeWXlnHPEFF57oiUGYvA8eVlSUXqxL4h5O4T2tg0LselZhWLLxGIwbFxV4krjoceKdqqSlVxmL1HQT1lDUxXI1PS2dGJIBbeBImVoY/5ZxHqbSuwlhktrDLliOL18fVmiaKzESIHk6hup8yfRyJ/oNKv2kfVwch0k7LbG814i+ofzn/8hEb1nl9vfVaR6FZNL6iRaxhbaUlrhJO/BENAqAGPmh66Kd33hK/Qz77hFdW66J7p/OFdZyjllYyuZqhINUshq4jvHpCeJlmqxTJRuA93aiCsWqfO9SPBQDPUthTGI5Z82Ic16YrHTlzL6/MMn6d6nlg1YTU3xSFvMsyaFcycY9WYg5DhcL3kww/NKocl56Rlm7nY7c2MGowjUIatAUZAU19BX0bMOnjeflzM6tLUh5jPPCi+OiS5sXMHOXTMzz8+LfG51negET3Q670SiN3fEPQKGq6L1fZVwXDVxZat9pTwmfWOadVyOVEiLRJAcL0iJRSlJ7f0xJsalyqtY9tnb6huIyDfVkJ4qPxn6YcwSV+8iohuJ6OXOuaPOuZ8kotcR0fc55x4gou+b/35gsfhRa8MgW2yAijw62fswF0oZF1G1YLaOCQqqlhVzWXaSPnwP2+0i6m2L5e5l3bFfPGe3wrmDciIJcm30YnBsPX+KpTnetm8d58asOmdxTH3k+CoKfjwkUhKsU0tAjE2KE+5fTwWv/9YbwqbwgXXn8955aRbb7py3wrctlogSQr05yH3m1x7uOslTgIwQUDkRjk8yjol6kXleE6UhGSGKEiuDiFk7CNoNQVlsbQbluFCZLPJQcY3nZkfeNRAsc01IeI7rRAcF7dD2JlvlTRfy3Z2Dq+1r6XWiY/dv+pCXnYJine8LRyPx91/+hlZbA1OJxbTh3Nz7C+G20jeoidYSlWj2/VZtS2siJO5K9OSnGnpF2UiRb53NjZHLshiSXyhyfPkeqeHk2kg2Tp7HpLEWzj0GRouT9t6/ijn0PWO1uW5IJfHA3otKuOI+PumjKku/WokrpSdaqyz2hRQutHBeLls90YJ9bprR02en9PWJ1HerePuJhmBP5Y+jTXL8OsrppDiISVe8P/RU9Cc9qY73fzdaJZfzUmn7xjKbj9h3LcR8/LkAukpOtLS+SSQt3LNK7zeV8GmSl+xurCGEqZhXN2jCHK+eaUP23s6ZqFE7ogIcPN6AWEz0RBfNXJ2yfQWe93Z+MTco87auEtrJFIzlQTiXz8HzV0UsNqgnelVFoXoOKdYMsTtL/S/KRhGK7tmjGwpLkurYI2KnGVpj59dvJZa4kueHbgxiO11RepJaz4BxC3uCUTRVGjO4xlN9eGuj1/pOlC4/4OoaafMXk8ZaOPcYMLPEiICWodZH2UfRQ55o6aNqFrx4zd2lthJDcUWsoHnPlHmJ8bFr2kEL1uuuv5d+/fp71P3iEJST7djGEXnuXp7o1n2kkKXUElC8Jb5qP3UT0iiCkrCabIBIDLfChqbO+LXemzaX7dDWRlQA0tYX7ysA7UqJK0EAl1ihv3LyIj0Q4RxAhgEO3FiGPvQR8BFzuhwGqanfLCuVIRRQzIkuS1hCDHm8Z0VBnpxIYIYUgQwoI1U7wfsb3zuwN7tSxEsPwsYLeezzoqTCy30lquaUc7IXMswRkcE7geWdaIBw8LykTUYJIoooEp09TlojIbs1/MZax2PhwkpDIV9CsbmptAZw7y/ILnKqg2z8R3wC0vtHZRK1Ob2cIUi7/3JGhORwaYWnPBjvYk8abd85+Xj7/qjEVplWvmwWnX+Owhoo7XdhjzFP9PCwER0Rbass99GKoT0ay9LcSh5bcKVFcVWlF7JzAyvbUND2W8qvrI7LFvmnzgruixXQhFFGPrVYKGlqyI/AojzUvfnrFUpwLyt8E86XQiymFaKI+hm1kJeoUCiJfN+64dyrXd+EinKsyvyzF6Un6fa9cqJXZPkO/Y95MX767TfTL7/3rqW/I6GQw6wooyFzkgKQ4onWvDvkDW2Uyni4X62YCt9fXnqYc468yDmYp/U9uOPeqzyyWdGUnoo9ryavGnnWqnPksHEta3xWlLS1sUEO1MaGivYqZKSMIrmsBOvSSqoSRbLMklLLPp1oNS1aDq2xKJx71p63MXlsPjZStMc0k40UIfqwwWJD0vwISiZ3e20qGFe/Xft+uPkDibWQkqqogS2VyESpcqt4imPyQ5g/i+uV/v6wRFbr2+pWCNAQGxr6wUZ0RGjCuVHdSG7BCyRQIimK0P6qYcapoSZDQR/OLVuVd4udewas2120vXF9PF4iMRgMRxs7Zxh4sdD1RSl6uFKV/JQalkSVksWFqqH7a78vztsYxo5rHubrSbnyiWGUWmgjZ7RA8z0GqZZpqiLc99oJ8EQ3bNayl5lIJq3KFQRZ8BsGaQdE+HlmCsU0K7xoWNB4ooNnBzOWK5RoRTi3ykCxBRTtRE/zDCjB0jzNCk+lbwzCsa9ELJE0ryDAvdeanR+QJ+oM48uo5wRDftoYVbChMd7+3FDJzMuaHBEQi3Fz1s8NTFpPdFd+mK3wfBLxV19PNKp3Dx01qezcuYf55ML2rc5Z5vuHI1PlNa+/kwBFSRj6w5ToETHLvRipDBdEIYdBY1mSBONVBV+0wOyWEh1dKKIlceQNK6VczSrQClkBOGStv6IbFnEkhHDvGZXJ0Qh4deRE5DgOSUbkSR5Y8Uvxe0SbIK4TzX/P0JsLFAfkNUOe5noTZQW8/gr+cOzcwnrV43tFkRer9kH69qZ5Qd7Lc0+6Vu5ToVrnNXwBUtmoJ05fgpk1sMxcrvFEo+cJfZUNZjuKvGqZsRwTeSHDgoYMrGoLh8p3601z7S0qwZ1waYUig5jTpbaJeCUIcW6geRruz433gqc3dr2SnfvQdjwlBq2x4Ti3x0zmStAG8xHV7NyMESM4RiRDpwfpB9L7awyx8vNxQFEm+nDufjnRSAnHnmokb3MEjvPjmWxQg55wkI4G19bCi9/urOBl9PBtaOVQgx42oiNiVvBCP8pRyAq5VmaT49jPM6exwi+0BxYYKa/6qbOXoPKnhVZgn2YFOUe0IVi1dyM/JAPWW+58DtIm6UlmqKw3UVbRqt7R4e1NJj0gWPJ7hnMBD5TGk4AYeWFpHen6shQ9ydL1wcvCeeA0BgIiLMD1VZLD/bl3nyV4SgcjFkvIOY6hj2Gvr7cZhvpLoeorGme6wzNTCEhZUYWJxmpwB1yxs6nylohe5FKeZ809cESKtHcigXiWyfeo2pkrBVLZvBKkoCiFU7Q2EQXjguwpQl78ZSW70w+gRMuGIlkJysuqrBinZDYlvOKG1IaUTl7juPcR1iE+XFnO6a3DuVklU75+ksnGoQyMH/IUozU+9IFvPyjBnCcaK4FV+3L/+GiouRGGdWwAI0Ypv/+2p5ozkmjTTOJ7UCPPR+9flmKoflaWYhQHmj8oimQmON20xj7D6rARHRFS+MhUuSDx4cjyR9Emq+nmR1R9W+2jahPXrFC1hsrS0y/++Z30ho/cr79IACJACpjMrfpS/ou0IA6F1T3RXvT4T7KCFVKQpTyrhZS0Tayy5MeYXdPCuad5IVtqkfDd2mSi4WjA05MJm1DoH8pJPsxswguEMcL1h7Y3RH4DVkkGAuYECGASCWIq87QWiFxrVfRTovsZE2D9XfG+OlZVx6xm9XcJCLDQGlR4Ty+55gjoi5yfHQR1yeONyvTMwFzNiqYGNHsPVaRW8HzKIe7SPlGXXmNChNt91jCft7/f+BrbjH9cEAdGjkIW9DVGJE4Jaoi55HBspCQiJfnQ9mZ8DQWOhbqGuWColDzJVV67Yw2tk6ygIyA6YmPD0SZz/5pYDu7P8TZCuDdrJCh1nmjELo7eD2eEr9m5QTj3oS1mD8yb+bVqKkFoX66xnXYcGaiyXE7pQOzZkie68J6ykk9x0fBMGPrBRnRESJ5oNKlrzxSzOQfPBvdR52WVv8T2rRZ2tJ7ofp7bsHHd++Qye24fBOUfhh1mBfZKAqv/EEBlL5bPByFFUvQCCKVsC9uiIscs5E04Fy8EIUuvNpwpGu4NLMlZLodeTrKCjuzw7JxtgZnrX21IYmqUIqMYe28gIMFw7kxWGpCnW0796B8dsQrEElc9vN1ZD7JDqQ+iIpzAmozSBBAjtsYbmhV8LmpAXnhIPIM80cHjKhNo6fKq2bDeohHW2XsocgDDHnhY6EvI4eUMGM3YyxvSLC9oSxHOHcafuxsStKGhMpPDyiWy07CGcO+l3h+gEtzzODDyoutrcrfo0UaJ5oA8mXw993b72IDBtYGUbBQun4FvAoezA3JLIJNKFRaImufjrs9AmgdKx0LyA1xngXyTF552JE805FfgPdGePOVFSdvMOpMX8rvVRK4a+sGU6JHQhGuD8Aroqe7niU5l6+0CLTDSdUTEfvyrolmIUWgcIlDB9T+HwKpK9HSFjbqrIjSWZm6TQd5MLIQQyYYfORwK1IcFlt5qDkplfECoIjBQZKVM6tIWcruA36OyvBcee9nLgjzRLDGZpERDBXEoYjGhDz28yn1KXPVVlGegPq5UukZjpJC+m3PTnIhwODci0CpKTzvAo4rymbts1lFvqpJYjFXGMnmuE2nzxHUpUZznkqjtPZXHrUpFQYo2zolGIZ9StI73nmYFH+0TUshCKD5nKGRzUkEeOvREQyVZd38+ZUX2BFZ59kCJlvYnIFPAfHSg6GAlWzbkasPVeSVWlmUmyj2QkwXR+81ACmJWetFIIcnjoX05Eg4b/1DOMmfAakjj5LHnIl7qtaynbGfoDxvRkRDyg+IfnVdvGHzojM6TzeUEzxRhd932+lixtEKGFlFyiYjGVinRyGKv6JMqdh2XEtMaIGaSojhfaDnPSQY8KygnGvU1A0LUJJOFDLQJIcMGrIWrEFKQUN30n/E0b21GPRlIgNGWXEP5auh7ZwVMyRON6hQrwo2HgFwnOryP+Le2PKwelgSJQfIKS+Vj+rEmz0u3te7rYmsZ+G4uzarrY2XtArpeNo65d0mI9MvfgfwNyWVkEHnSYl/iJaxUCnIuKwREzbiHZ14OEq3q6uqIxdpPHC+5pTH8on1ieR+OvB9BCWmza8fuTSQb9h3ximjbyBo7Qyvz9M3rhOSJRSEa8zM2EsnXx6WoBVRTfAbDecMaLitCvBFBF23EGhnAHpOB6EVNpIAjT9sbnfb94jrIe8Ln36uQ086FiofjonwBjOxICc+KkrY2N6Ml9wKPA9e/hjSue3w+9+Zjvzh/mnay0pMjPpLOwrnHg43oGHBOYRnShe7gnOh+oTPBc6JVjJEHQrpO6seqCGGdksUxtCvm5QGFaiiIRoSIRjUreG8nkRwyhhhp4ZwEYaFZWS3SvCIo50siIVHapIpQ0g14ClCNVXmT5Dch5OnVCnhQgBUMIJsbjiftQUY1pZVfupZDPGeaeQ89y/fMBG+3Z3OF+Wfi9M2+5GFLnvzOc07zkumnU7Pas885b/uFz+bzmbPC0/bWJjlE+rS1Ib8jRd6e6BEv5TUotEGkYGlW5DuriMWYc4rSUwm8882aGby3PGkUUqKRkE6kMDSKvA26Or3IUMd7ouV0H+ipBOzc0BM9JxPlZIMZYDdG4dw58EQjTo9ZjlPMiITxA0qqhktAPg6U8DIosfL77RvOjYwgof9STjsi1RM90TGZsLUWtquLcP3j2g9ONfjsIBVvG/Dd4CgGC+ceGqZEj4QgwGGrar/QHZQDWYdRM+Ejmlqc3f5IjKscJkAxWxX1Qgw86Kjm3m6xczdeFZ0RYQbGWQo5aohykJDDzKmQayhYqnFOs+yJ7pvvrcpxBELSJJPHNhc8ZDjnWJevx3vxKwGQ9fLkpZhvhUMlw/3lKAOp76jtVPRlxuawm8RikiLsEzz9KMyPSBftwnvZwnFlmgwqMyfUASZqs2ZrnoeLosL30AiNtdLApr/gvWsCFL+AygsJzlGQv2miebjnmdThtEhJZdao+XHWm4ZS0GC0jtw+CuOf5HKd7UwIp62Po0glYARGKQZSNAg2xOpkRt5TK1+POE/y2uikMMQJ/eNz7lE0VuXJlXLaJd4B5KBI5RuoyBuZdwNC4VE+OQxlV0aqmSd6eNiIjgRUk09NLNb+KFurrtbzxG3cmvIebaBcLA619Xqgj3dWKyOyQj/JC6B0KMO5ExH6G93YwvtsvVcp77YsvZg3rM1pYvNqoSW4vxKsOT4TNqmGdETexORwbmYOe0+Fn4duAi8/Tq8AAqxgia7CIDkBsPIkclgmnWlLaL51/zgWlbxOiKigIIbSXqkI4W5sHxglVAxh7pUT3U/Z1SjfaO5Ix9B3RSSv5ZlAcknUrNNIiZtI3hjvKQeKRHdfdC4e+kyElT2p5jyKumm3c1gIsSSSx2QKvuumPzk2/C6tf54Jp1/RmzZHHd0mGPKIZEMckWDoq8dcEW4cuQVSoibz8l18CS3AmZEjJRmFawNDFKp/nlfhxmwkFzLUKo0MqTnR0vGtTbfY/9b8bPYgef5sMoouioQInt44N3dlwIt/YyEcH7FvR9bItswtcDkUZSU/8AaCireCLe+FQtmBAUsb+bobPECXG2xERwIMr9DmRPfOj5E9tqJyx9yvF7FYaAcwk2oRciMXQrYiu9I0kzfMKn93/Olfk3Eo2xJrh5fynEGGm5nasMNvwn1DCcP9kQcKhQIi9lPJcDIR7t+wWwKjFuvl0eWzseHcCtI0jSdarGEKSt+ge696HYfHT1+ioqN4oxqlHHGXXBc9fqzd9pI3SGTnThsjtvSPok0xDQFEkBDNo0gSw5+rvhagHZmcCdXCDffY2nC06cDeqDAKaRR6rr9hTBBBZdUOIBbLPRuKGTABnA/VOd2UmeaeVdQCrggiRcMQUV3BgL8e1Tnu5zjQEFOh6AOR2AuF+4JIgAzkPE+BAWlayDW8NdFE0nHIzp16HBB3QscOMnLnJW04oUQnCrcXxt8T5mJA4dqaKEDeaSXL45gULoRzA9kPRilYOPfQMCV6JKB8K8SWV1vlEonF2HDuEB6iVO7QBhawRFgTCBEGZ+fGNTflBVsWBodCvXgqjQhVWTTZks/mRAMyHeSJRp4GyG4JhRzeUl96L4ZbNSXdZE8B984Ru7aW/bo3KU4mC7BIAMygAFLl40nEZRoyJ+7e7LEV6zc/cvw8vfY9d9If3/AwbqP1MJxCLPUtGJ26iIZWz9uSnzUt1JuLntF4uDUlruQc4ZIOi0SLQTHFObmQnVspaHtiStkJ0TbVccznEbx+HLFm6IunZmy7fUF7eHUPHevtLC/FSJJwr6YvUsSKTBzGR8PocqKRkZVnCJb3H2j4V6TEpBhpUbgvCtfOQDjwTFCyiKpUBkkOgjnp6uhFTpGT3w8ycKE9aAIiCaYZqlFeyT5iOD4wgnD9k4l+m/5xfUeEiDPF/i4fB57oUlbCURSBhkfC0A82oiMhK9GCpgvxY8sJoNArGBInk0TEzuc+QIkFtwkhG0ZhDQLUBigUPc2x1Xc3FpSwMXHW1aXzc97bi4S6TJHTLF2vCSmGxGHM8aL0Yl5mDrzsmpJsEvsl9OKX8iaESNdwDVRNOLcciogEACkUEdUwzQWP7pC5yk+dmRAR0b1PLdaN199HF+ZNxOdE9zUKSG3FydUW20PCsdQfjQEkzJ94eLPsiW4UcX6dCqH7MGJCkXstERTOlkLP4+zccuitvP63+8IbJXH0i9oTXcikUkS4ugFRiFjpuwahcO7qWY4AJRiFc7PeMG2dZ0GmWTkcd6F/HoZzo1rryBMtHi/KZWbqzvVEmAcHlxDj9jgULi3vcahE2DQvaGvDscRuk6yABjbZkIyI4fjj6FtHURzIwFA7pUC0ESIW456/ZkZH4dw9me0N/WEjOhIQU6WmnASRIgcTEYuxG56nrQ1ekFm+H79BaEJBh/ZEa84TF2whd6wbapqCsPhp7yjlatc5fL1znkra2HCsNRh5gLJC9t6Lm5AiVFw63vVEs4oCZI7lNql5/0B+JCQe60ksBgXAUhYgJEKh+rhCuYleKyqWq9WI5gis+pKTiUp0Hv/q+pJ89VWwm1rxsgdQ6g+qr06EiMUKdm5XfcBh1svfwPL4ZohcCex71TlezN9HubPhHho2bCIFGzQwThFhzg9Nf5ZDteN9gtE6KBqG6WsolYY8xYicsH8Jqyqcl9uf0BqGUrSQYV1TSx1Fe8js3LInFXnqUbg7ms85KFFVyYREm5wnuMQpcpL8MMnkEmMoki0XjOR1/zh2bLT/11EcYH9nHRyIyFfm8tGQqlXXy46svt+eoT9MiR4J6vAKaFVOy6mWcjC1ii3MtVKUftHmXiNwuZGxPqH6nkhZHQLa/hIRFV721uI6isASjQwLQJFDSrZkidf2nWevbLwUsXCvsvTzTRbVMO2nxKPvDeVsI2IxJACmvjvkxZFyi2Ulb7VvpVsDWNOGRB4mhVhz4dwaz7qPKIiSgq0ZIxTqV7XbvS9WkDXe6iyXPdFIQO+2wxlfEQv4TKGYIoOQhvBrVsher3Cf7Y0NNqoJETWFvkqKB1G1NlXM0DgFSRJyA3kRXGNZdm3ZkHdJG92GcqKBYV8KF6/W9+hhmmZyCSlEFponlrhC4d6IOR2Fe2s90cjoIzk7KmIuTtGUw7WzvBS5bdAeNMlKuVwcKBGmKWGFcpL7cq5MUfkvoOROC7D/K+Wj3nwEA1fJMTSwER0JWV6VlIHEYQxbY72BM2uOuk40mxON61YG5OXcSgc252g7835IuUirYJpFBOqIkD1R1GRELKZxrOalngnKSRc5EEIQo3qWy4pg5akFlmBJkSu7AvIyizO0hCJm0J75YO1NLMbeiS3RwdMdZ/eE35vaE70Z7Z8sAHpYfgXVPZ8AAS9nFM6qb7owZk+SQl09c82R0JmHIgO4EBki9Y17pkVlWB8ePlnoo57BfDnfWN9mdN53GZuLgjY3nOClqebPoQUBttuH2PxePGei8IqL3ry5kRDdQ2Lpr47La0noBwznhuGpOgOGpnQVkZxv3lReUDw3swZrPNGO+Fqz2DMvK+FqciNh35Xe6QRF22QKXomYHOIDe7OcrjQD4dwTlMsPDCm4BBjgRAGKnhwO7VXEaqInGTx/ikwW1g7ZyOFpQLLBAAAgAElEQVRpayu+/3fr03fPgfs38BQ3+5qsxPNOLZ2Rn2fnbtZVTr6QmO0N/WFK9EjQkmwgxZQruYOYDOsNT/RK6j6oJqxNViDkfgzz8Wq9xEi4kcLi+oaWxjATDAxd6wnMawE5z0Fp4Bkk+TqG1f2BJV6TkwbrKMt9R9EByAqPBDw+lA1YqpXlRzCxGO/1QuHcKV4YDSGUdG/pvqugJkhZCufuSdrVI5x7Iq1XitDq6D0lxbwmieRL86A2ReEczB0inO+5iscbKXpySDj2eKNSQiithWhO4oRqXoM85Zr1HKx5uLa2/O1X98HPVOc098yJDso8W+cZeLMm9ZjK5EaSzCPlzKI1UFUjG6QjcCkVREEmkg1EXPuevFhZg4jmxFryGk4kG5LFcHfgbcSedg/qaMuefGSonWSFOP5oj5oBQ7IU7QE90Vz1jZpwEo2tLOfinGg5SgOFc6MoRPRtGPrDlOiRUBMV9cwRrRdEkc1XCn3C4R9aT3RT3md1T3TIk9KyUyNowqND+LlEgFKUXrCYr5bnKWHGCPIxwBQAoMiF69mcKLCJIvIpGM6WEs4N8rVQCGcd8QDDuft6wlHkBxaANjccyxYMw62FULVwvejFaZUmis1IiRxQQ3ylneUzRlmT2+gXRs0zegve5pUU9uZdSobEcE/JILoMt3Bf8bsDSmeowY2MMEQ4zLrqyybLHp2VJR3acsQpWtOqXCpQkkE5NkV+X+U1k4XGDOQph6inw1ub7PxGVSCI2mHUOB9W5YEHnl7kCUbeMklJl55hNl/j+DrOaP9QGAITom3Q+0bke1nhYXUHVIpOjCaCjpF5uPv896XyfJAzRfYkI/ZtTYkrKVKgCueW5zcy4kuefCn6JAeyFQylV+ajs3WgUbg3WOcb4zMg9RMMWEYqNg5sVAdFs6phtjzsWeNCvYmwVRZt3FXJDd3rn4DNWwxzBHlUq0Lj+Qrh5/1J2XbJE93ZBfVllnhv7qYj1tI/QzVcgZAiCRFEspCDNinoRVfkexFF0gZaoXrS9U3NWLmOI2cpDgICt8dP84KY8qv1cUT8JZawggJqpWRLuazstUMSi7Ge6JI4VbxvDWvOMDBGGatJzufghntKNbz5NjErvcYDRyTnw4X53Vb0u28DCZJNGTmZnJAIhHPDdaZKlUICOdpzEIO3pnxVxTgMcp1BibOqLWwYUFfsADwsErEkkbxvopxiqYTRJJeNtIidGbNzY/JEzhNcek952ShxXU4ETxXnxrKBYr6/gMoZ1bch1wqvjACbAqFeMV/DZW+mVIFC9jTjnONqD+MifLAnGpVpRMz/mJ1b3t85A1OTr985PpcfYDpZXcqVkU9qol8URcL0X0EKJ/UPyQeG/rBRHQkZyF+dZIVstRVCY6vrcc1eIj4XeQqseov3Al42QQgMAtzGUJ5ohRKtIVUjwl7FISApAF2ghRCR0eUw3EnOKYJlkEr++nzu3e9d47qQlVTkJZkBY01qOLfGE41IVXrngxF+d1MooMrM6rInesBwbiYnWlQke4ZfpxCLrdqWig8AHBevRcYvoHxI7Yd7hHOkWuPVOXzenheMl912pHOQUWB7k1cm2ue00fXczXIvp7coS1xJimP7PpIXUDMuGsO7dI/uGrk0HpDcEefMyvtL/0im6jgK5+aVsJrcjfNUtkogxs6o9wdufyqRF7hSyxGxmGREgOMHohchuzUoAZYLSioR3sOC44YtbQfWsbzwYv9z4f0i0jvtt8MpuQ2BLiPPA2IwWJ4sXM/I0VkBokCAcdPQH6ZEjwRNjkI7dG7Z6r+4oSyH7shKdrAaS4XrpQWpey8iiZmYFwKHZLpm79d5xiDsNhtC3Dveh218Vazy/LBuKVQEZaIcHNKJLfmwHAzMieY2eFQSTvbmheMonBsr0XJ0gsgsC7xnW4IAErwQHJCXILX8SwbIu7hbswo2s+4Eor2usNZbse2RE93f2ywbGvgoBpTLyisfqDYs0Zw1XxH+jAxoyLuLPJ0TTb5zLufNVufIe5uqfFVLiZb2QPmbktek6hwc0RUz6nY9nSjfuX3O4vi2UwoAw3DwRLNleIDxGeR/54oyfNo6z7EvF5VAaiuhy+MrPxuuw7s4F7r3x+W9ZE8kkeb5cLj71oajTWZOF6VvotQiAzwDkQYz9L0AI7ymTjRiR+eOF96L8w97anXfDl8CM8gf8v25taIbit+V97OiJNcK9e/OP5SqNxUipfYjnHOvdM7d55x70Dn3Guac/945d7dz7i7n3DvH6svBGdU1Q1do75ZpCV4/bkkKoTFcOQIkNGtqIoYFR6ggU7dFJJXvwezcQ0Hj+ZoAoRF5d4b0REv1d7tAxC4opDkvylpAitdRxgstzokGmwxraEHhSlhJra7vJwA2pDfgeBCSugzICnZuJOCIzOhACUfEUG2jW+x7niIlvfVdxQx6KJdLy/o5iyoCSLHVlI9abp/79mSlPN5WiLSQ+gFTd3rUCNfkymKDqk5BQ95dWOYtGAtAXqh0j+ocOcc7EFRJQCXhwn1EpQHse0TVvJXK/hC1akkHpT56zmrRAtG+dI53BW0Yzj1XkviUFKzkiOHcWQFzZtE8Fo9n5ZydOH4tkeDJq9mb5XDgQ1sb0ReISNVQicPQx/bzxR0ryBMvGTmq98N94rlQZ5lIx84tpxjK90epIJISr41Eg0py71SJeIRV9zi3VgQjJreuoXz1Kqqmv3yxn+Cc2ySi3yeiHyCibyKiVznnvqlzzsuI6BeI6Du993+diP7PsfpzMEZ1DRG8gpwSjArTY88WFtql0JuQM6YRfScgnHvIOrIIGqW0sfqh3KFViH76YZV75Z2NOGaNJJJznnaYcLTqeqCIZbzwWpRlFaqJhP2edc0zxM5dyIpIQ+whh3NxHrtmk+U90ZKAOVEIeNgLET8eiKE0zKjsegPWEznvmFc2ugoCAufdEz3AGvb/mBLNhXOL4eGrK97hOAwVFKI0OC+2NhRbEwaMzoEs00CpROGkVV99xTAsKMEwnDuXhflwDgqznoHw1aDwI++Z2hMtPXMhK3Ht+6B9WCqBtCGEfCJipylQgrJczrlFObPQ0wrWyIlojJq/A2beNJ5omXmcL7GIamDPPZVineVynk7Rj3xykslKaF54sf02+3bXAFMf35A9zeN6ovnvdQY8/ShSAH07KJWlWyd6yQg/P85FCaB3j5jRs7Jilufz6Q9UTvS3EdGD3vuHvfczIvoTIvrRzjn/mIh+33t/iojIe39srM4cmFFdN8xQfpDGqgpCe+T8GbmcQAbySxbbkjd4ScCsPdHI3a2ERimFnh/kURnSE90rnJvbqHE4tyScZjkI5y54IQYJEciTos45AiH26P5s+RagADSRI8wmCUJmNd8rN/aBTR6G8fdk564Jn0C4OHvvjBee0bfWxaz2iq6QEy0SgQXhZfnZeHZubamq9jUoH7Vgy+PUHlrOECEoD40BU5hboH5tYESXvv3ZPBRUAvKKI2MrUaVsHNralPOZUZ3oQi5FFM5BRoEMRWvNx4Rj1CcKZEmLx2OpV0QgEkBlgEBzEOwfIBpmBvaHSSazS2clKuEEiMPmgr4UfSfmHHu8f8ASQYB9m/dUo3SkoMSnOEY0Rgh+/PLSi4alDHCqhJzjvtGRaP5Ie2RelFQK4dpdJXbp+o6RnmM2x0o20z5Y42dzUjWRNE4glYOkbyBVbyZEEa4ptpxzN7X+vbp17MVE9Fjr96Pzv7XxDUT0Dc65G5xzn3POvXK0jo5148sdwXPUyArLtUPTlOSSjohMqmDDLMqI1VjOIexHjJMv/9F7rgIKRJTtein0dnFB7TYFrY4C2/iqmObl/CNr3yd+zzi5VXOuhlhMtOSWSEiqhJwzl9p/rdpH7Nld735HrITe/6yoiuaEZ3ORnLYd0UovW6IbYhDeAEEkzXF+gw/HdcRiy/M3sMnzXo5uakjnBO/F9QQJ1+02uOv5PMlOiOhy56Lnb3bWnvYasrSWLXyPHf4DxssXhC6pz9FjrCe6mV8xg8M0K+nQs4AXTDDqHdraIIosldO8yoWLC1DV883ygg4fxu92Z3OTuCfXeHeXvTHxd7vwnXTmwyyXmber/gJ27qygq4CXOc4P0c1jlcn6ZkVE6fB+Id+/ywIeW55qBU6R74yUWKJuNE3zTHX+vGAIlPqAPInTvKSdnUoRcJH9rCF24uWIa67YEe/PtV+Wfq4I6PYf7vj2piPnYl7W5huJoU43ApwabE52VpKjSgnkKnZAJRmGu8tKbF54umLDUcYcr+ssR7pXlCXMeQ/z59xkeSHLi5JKj3PCWU9+EV/n220TNfvQchSfJ0eeDjPs5suRdByPTj++GlQtABmY2kpylPguxykw1xzZZo+vIXLv/SuYY/xm2GCLiF5GRN9FRC8hok87577Ze396uC5WME/0SEBWXa1Vlr8e5ExnzYYZz4+VrZLdvhJJSme/Oq59MBVK8dTnwHICOq/mEBDDzzuLOVKSOVKmdluSJwCH6/E5a4F4SmJuJcJjinKS+WgHLGBU/euXE5UVJW1sONYbh/PRUHmWVkg0awXnhV8iPtQQtd+8GylEllcoYt62pt0qP1WfE90nT1kRzt15N33LYnHHQqRFn/kB1+q8ZCMgUJhffU5iTnTcqNq5DzSm4nYy8B158iL3Qt1X5GVWEGei+8xAzjTR3EgqhLgStcpNglBeItnQhdICUN4mCpeFa1y3RFFEUZEMMcFIKxlCcSRTP0NhY8jajH6HGTASh/2PZWdupamIOdkiMZf8HUsRK/X1UgWGEhGPVmz1sdcTqjfUMmWMd0OYP00JSpRT3c+TjzhR1DXWu+M/H4wp8kTPy+6xxGKFvJZgYlFQngw4SNDc2mc4SkRf3fr9JUT0ROSc93jvM+/9I0R0H1VK9eA4MKO6bshKHNoihodqFkwgOEl1pjOFgNDuK1E/pXPGMOT2hSY8ms1/Cgtia0GNsoAOWuJK//z1Ri3k/cCcZ5BTxB335CtLMOflBky1mrzP6jiT710GLwsnIIJ8MCCoItK2DFiKEenJNCvmAoikKMXVqAXhN0ZaAwSEvKi8BOy7Afl63vsqzFZI12CV6BVzraaMciuRRbW/Ry4Mr3upRGjYh8QMlSmq2Lkl0jkUxsgbKKV5H65HBhIi3sAUzqkEQO4cVxM2otxvkYujkL/jtkfPcwzvrfQCH+mvhkOAqO0d4nkUpBxSovDugAdfRSqFDV0aRXJzw4llLZGnG7HAS0oyyplFxJVSCSeUSoDHZlGJXdp/YB1esH/AnGh5DSea19HumRNetSGnCBalnLIhhQyjEk5V+7xMWpPaMXOzmEca8JwkjWMktiygCgRZURKxET04ikNj6JbDtWUDEzJgiemXzinYuTHR4j7CF4noZc65r3XO7RDRjxPRezvn/AURfTcRkXPuuVSFdz88RmfgqDrnvs459/8554475445597jnPu6MTpzkIDyTydC6AqRzvOVwgacASbG7r2IMOlCDEN7olepE43KIaGaxKmQQkpjyMA4Z0XJllggmrNrikLOPCQ6stTnLXZS5yIlPFYM5+aPc0qs7GXXeqIlYhHnJCEI12FexYuzEsO1MgKBDRUr5euRJzovPZFQ37fyogue0hXqT86Y9UAixxKJxZhjQ3uim5zV5WetctqFMlWdCI9VWHdjx7qkNTMgXGvyqmeAWZkIzyOUNx7aQcKipq9IUSeSldZwnpRbrQpxB4SBRLpxQZ6u6j7yORririRPdC6PV1bISlpXSV7+DmRDHlFjwIlVO2kfX7oeGDKgkgzWaDRvNQSMqeHamnBuWYkWco6BEaAoPRWlZ+cHZIZXRmNJEVHS8WzeN5QOJkV5OMdX20HEjJUnuvVsEWOwXIISVdtB1TtkQ+t+gvc+J6KfJaIPEtE9RPSn3vu7nHO/4pz7kflpHySiE865u4no40T08977E2P0R5MT/U6q6MT/2/nvP05E7yKi/3KMDh0USOGRRLr8o+p4JFGOFBtmVtD2zgZRJAvOe09ZLjM1dvtChEtnRPuZl3RY1QoGKjNTtwlIHpaEwe6COL9eiKBUAYaFd9qtQqY2xTIKksKSFZ4OMbmRRVnON7l4OBbKCe7mhC15BBXh3BIrb6Pgz7Fwfw8FhFpI2dqgaoZ25mhdNid4IpaNBKjGqVy7VkGqsrlBVEbyvTtKzqo1ZDGpG/DitEl1Ijqu6IkGxo3ltubPFhFCkIFmlWMzSfFe+C6Xcxela+KltOakRtsbNI1dW4excrmilSEidrQd5setR7gkVFuJ44wEmtDlSpCT2J2JqnU3Ng7VObgeOhEu6bWQh8zsRyg8HZXsybpsxpEXNAHkZKG/RECBUniiJ519KybII0NjfA74up8yO3ghG+aLKtrmEnN8IniigyHq0NYGXZotf9NtT6GUd47IQqt3sPwNQGIxUD0CkYJy3A3dczBPTud4aw5M80WenC5ygfjNey96MxFxV5f0jqvTzSnRXeKumIGl3f6SIRGEe2fzSDAOk6yST5bWNh++jZBWE78e5TzPspKu3HTLSrxvvj0pCiHLZU/2rChpB8gnkpK93+C9v56Iru/87bWtnz0R/bP5v1GhGVXnvX+b9z6f/3s7pbItXQZAFnepHAMRzYmC0qySPFOhp5JWKUuD6P/7MeuuClYp7SxMkMgHhiVWfd5I1KJXza2uPY7KerJdRTYX2DWDkswttA3xFpcTLR/vRivENtGqLjqnRHs6tBXZZML1CqMTkeRpCKzA8etRjdOLs0UvTiysGHEgIIZrFBnB16CUy4sF4ZuNcECRG4K3TQrBlPq6/HecD7fKMckT3Y6O6c5Tnp2bV4RqI0UP9m1PuDQPGl+N8kMkK2iI/bXpC/4G28JqV1BACj8y5hFVxkSUekHEC+xElecsB2Vj2oJxbN0oSg9Lz9D/z97bx9y27eVBz1jvx9777LPPudwLGAulQK1aSiitLQU0xYIh1iqQSKJGarAqNRH6gRVr1BqotootWkATSYlQQ9IAigEFaWz4iE1BoJGCRQottFBvhRQu93LvWWt+Df8Yc8w55pjj9zy/tda7ufec847k5JzzzjnHHGvOOcb4fTy/54FPI1hlUgErW7YlObt0DcrjZPuLyrrXnBstci2WyY1R2xhqzzbXEMnOnfdewb4tdILV/qjI5a6z+fj1TL1jcZIFp4htU62B2maQw2uTWTXl4v0t47d0wEV5R87U2hxDighYzT1VEy0y0eNESyX6Mb6tnOgPpeZ5qt8TQvjjIYSPDSH8hhDClwH4X0MI7wwhvPNlD/DN2vpxMmvkgJydYDWe9aSqHUVlONmTxmNk1GMFtLQGu/YhmtcpVZH/JSjwknWiz4WyZzichRDINUO2luCEeyvTO3FHSWeiNXs1PS6CQimLwb9nT7aNGUn0/kLj9G/9wq8igmUDNSmPZaCpLJSS31JZmE69GxJAyfJbrBb4HJhYdthbQRbGXm01E359geOd79Wqs2WOkILrMudhmOKsv04Iy4hxFBFn+TVPlpM5t776X6VXDWCGTdrnqJroGAPd2zpB0uOpP1Xw1HQfDk/29JHGM+LmEHaM9K0xe0pWnliQWSFzw2TY8hhYoPE0cHbvTObWuj7rOEsnWaJtLuTkkEHWfQBoc1yWWun9L93/CidZ6EArOPhAdJZ1OdRcbiXg2MrmtK5XElJqTh9lkOQ6pJl6N2r9PInAn9pH22oD5XGHBvejE/1SmgfO/S/N//6D1d//ANJe/PHn3jSE8EcB/Jvz9T8G4F+PMR7P7edDufXDhFeugXOTyFeGPlHDiQjbL3Bn56Q6CkIrBue2Mk+XNG9WW20Iy4b8kmuiz3XG+5Fvkp1g3+znTbJVALAaCfYmA9xIHWXtiLGMGjeeNVs9gzrOhqphAQ6iZqifuIH4+it3lIFaRaqPc31la5FTrMeK1MXLWSA1sm/2306W37ICDAlhcLCqTnatGyOetP6eHbSG/grTbbdIwnhNdPuaHDBAQwkkX9OCQyoH6EQcxwUGenuD9xpjZTC/7IQr0jsgBWGsTIuSPwI06mDVAD80ywLyfZ48YcGs4lka31Qm8bHHMQeJKXRWsyWrZ+LpA0hrlyL1OQ2Js2FznoF2CUZXWn1hwj2RQmOympn4icO57TVW6Th7S1JUIF8Tixnjy0grqTN8WRBaOfEAd5JzEOJSnhxFtqcyuQrOrQKJKzv3te/PyqRz4sfEecKRIGyd7VTN8jDhmVhLbp9x+8YrcdW+vz33pinOxKOPTvTLaNKJjjF+3EPeMITwUQD+EIBPiDG+EUL4ZqQ66294yPt8sFs3RrxDMRleCN3pxkkbToNd77XW5/gmlWaFFFmfB1Ij92a1W7DC7XFeM/1wTnRhSbY0IarWCyOlG0Wkea55btWkLeyjhHgLIEbCJBy1poZpcVxEQtMmx9m33/mca4zSSK1gy/ewc3/0O57Zx8kmF2Okhr9iPe6WLICVgRJZnDMy0bXfImF4ObjhdKJP/YgXxhiUfFrzmAXn3vzdV/ecAwZsDK35cxJQXMZfkYzvQEnhFMwPUDXE3MBN53hqe0WQz+EoqDWsVCiw6qpVRtRFcObIIvfjhFvi8K9zQ7Gn6yzQIoMWbPi4B+7L0DzJyRZOnCR2usxJK0vCWnNWwbHz/W31Bt8apzKtFnpQIrVGLkXXIhbbKG3PATwFd78Uzq3I9tTc7cWckuSYMpN83fvLHBjJpm3pgEfc3diGqC8TzckMXxNyeZrUT5TbkKAgU+dQGtaP7bom3ZsQwg2A3wfgY8vzY4xfdeV9n4UQegCvYK/x9aZvCV5hbDjTRAySuEjWqMySJjK6AeJewKNckDyAbpZJAexsEIBEJGR+ZW2SITaOJjnPjmBlzhptSGG2BBz3NwcciObrWc0Y/7lQ9lwXbLWun9pO6nx/po06TNkRM+4tnGRuRERHJtrOcgCpnlvpHLJI8VEQI6VIrcqEr8frz4xtstlJtmvZjU0sk4oI9uQ1w1d8G9X3zK6XmegiqPT++tqeZwBOw4jnT27RTLE3WjdLjez6ccjbnHOMIWCugYAvz6F4/quRYj//F08bi+DseAA39NrXnjVS43MbHDXEy/dFkVEWXLD4zhw8HYBm537uIBZ7enezOtHFs84oLE7QxIOkm7HetPcIALMMpK5jVsGHYz9qJ1ogbVI/rXO236GqieZwblsiaX2maHLT1fMjVHt7abO0nWi2f8T2t9VYA004sAPuC9hzRLFH96Ntr5Xju7sJbZ3qMQXwNNydE88pJ/b+5qaJElkzudc54TbSgM2VKPeoLZKg9f2UChb7H9hPI+5vjCB8jC7JWSUfert8+/t2FEF6RRTclriKxfEpzc1Ga651j+3BmuepfgeALwTwLgAvin8uajHGvwfgzwD4uwDeDeBXYox/qT4vhPBFIYQfDiH88DA40xwfQm2YbDa9k8geKE3bWnO3eQ6JOivN09b5l2SiEwv4w2R1AX+N8bEXTvI5mXWngy/7cTRVF5y0fA1HDpFmsgcBx1I1z0umWgR2GDmVirSyAILSwGQ1tUAKEnA4tyYZsn5bzmCq+XqxxNWSiVbrgTgu320jy1plAFqEamexc1uZY0KKdxqmnQ70ch3pzx6DQR7m0I9uEov13ABnpTtd/ewb0j2KSwBQklBx1ldlSAtbxiw3fyaazyMFWwQEbDb6mK4VkVA6h2XN+Zrggejm81gGOJ/jIpATcHoWKDkJZ54HsubfKtc4i9zQR/ylJRI5EupyOPCEwyGYQeh+THB7O1Prs5E8xFzseiWBZhKDit/PkDaAnttaQUIopijiOEHOl4MAFs9PYrdmQUSH5Cwl7hpp/50gFlPzvyf7Y0aBSPnRM6QoH5u/eYC2Hx1j/KSHumEI4cMAfC6AjwPwHgDfEkL4gpn1e2kxxq8D8HUA8Pz58zcdGzhj/lygSYJN16wRFNCXnMm+uzmgFX84FtAXz4PVTufY/JDcTqSTBfscYjHFcuphQb32o5M13NXv7onEBDAboC0jIQSMc22kqvlSkXgzW0lrwsICB7XqhhWkMRFzXU4sduxHvE6e3eDQp7UMqHR/20BRpCrHwTAg5veva5Z9km2KKM+Ei4+JTKtlgOdrrWezgaFtJoxFcNf8M07DiHcY0NnTMJlkSqdhSrfdSZ6cn4k+kvWFEde0gyBhc/zJbXut9dRSrsf2z3QJuJL1bMmEhtAkTcv3WQ1URiLH58gNIUYEfLwOgHaG2Fqyyt3xcipg+13XAaJuEGRBjW+i9Y4VoVfqi+9JeczM0O+GKaFCyPVM5ochzkonq2GSrGvcbdgmAqs1TrJrC2KpS4+bEkZzGwQ7fQ6yWoGobhRovTmTabI/F2tIq6REoY1KibD2+HggdjlOOVM4NwZg70F5bVVBGBlINjLNHvb1J/difzdsq3RcEYtxDe6TDMgR+bc5QdJG34SlvPP+dl+OBRSB2sdM9Etpnqf6XSGEz37Ae/4zAH4mxviLMcYewP8E4NMfsP8PiUZJHGTUkUeOZOZK1EB4mCLr8XikTaxxPFTzGFB5PNohO//3nNuOlsdgtE7IFLBoaC82SX1cZSvT9czIUhqXyolVTnJ571ZGlLHND1Jax4ZuXgzXzsdlJloQi3nr0UhNLmAbOEv/jXerMjgqU+ptCa5vj9+aF3ZNtB0CM7WgXZnoFju3Wq/t9bMkFjv3WkAjSFIfnGVa1eyX/Wzu05RA4gFRxWLf0ust53qLJd3SrPdmkRnjPltTztmP2Lqez1FwbrXGdiIIy+Dc00wcdjGxl1rjlI6zJBbzHrfn0d0Nl1hkHDG9QjqNURJjqgAG4Mi0G/dYCAZNJ1jAtQXx2qrDLIIAYg9SaCw7sTRfb0p0jThQYlGHvJsIUPF1WLB/93amWgVABpEgWVGAlzGnP7brmuep/gCAbwshvBFCeG8I4X0hhBaRqOHehj0AACAASURBVLf9XQCfGkJ4JaSw3mcB+Ikr+vuQa9MUMTI4t5ILcjJVqqix0qVVbKzl+TRzaxqlD+xEO4yjfJ4kbRML5kO0czWye+KoASQTjZU91NQGV3BuB3EKO64itRKuNPglrlpftSfTTWuaiASFgmsrZMmpzNK0jgsoo9QPF8RiO8jwrn97E1bSIWoueduJfNvs27FroklW2YJzk2syj0IrE6UkABlUcNGnJfPKB38WhFJibgCe2t7VkGzZqkcBWcywQ7UOAJif9f54zXdxiSYtUO4l7LkJ+LPjPvlemp1bB6MUgkrrRI9USg3QvAo2OaLmTgB4oMlzfwUXZvsTQyYoCSTlhHmQVHR/k+SEqsxBvb+8h9iZdHZcBeElnN4L57ben2IHFwG8fuJzUKrlONZQC7WyBCmJRjcjCpYa3U4JWraPPLbLm+ep/lkAnwbglRjjazHGFzHG1y69YYzxBwF8K4C/hiRvdcAM236rNC0HIJxg0yhPC5k3aqvqkxg5y6Y/Reo0jIb0y8PJW+X7AFy6JN83bUYW4ZfKrD/MuE/9tGeoKtuSQkn/HkTdLquJ7oUhPCzGOt9EzdrNuSbMevYeDVkayRW/XWVqjv1IoZec+CUzy/JNTNYciSyLOR8FlLEbxvTsRT3b9veVpDvcwFmzfxmWvSWTSmO3jSevTnStDV22HflSNQZVS1m3XCN3zjWlEx2qsXrqRZmjrwwk9vtYLVurzGI39rG9Pq/jmxDA4YbpPOHEKXK/qfrOUGWRY1yCgWpv9EhP8azsCASuvdqJ7GTeR9V+dBTOeBpPCw4csSV2UyUtNqR4nCJVQNA1uXwdWGtefei53Teq0HVOJ58lJhikPtWcaji31QYp0ajZmQEHOlEhWkz7QGWaZyfZlLiyrp9tUuGoLXvgxUEcW2IwjS86guTKPmkcL8g/mZPdkVKJFIBY728R/drlmdx2W9c7NTcea6JfRvM40T8F4MdjvIJhqWoxxv8kxviPxxg/Mcb4+2OMlprFm7J5SRouNcrVgrrqQD9MJvqoaoiNKOBDZXTL+wCNhbhKR0iHS9W3PFgm+rx+FKT51NvHB5HR8tZEszp7VhOmSIdUxrIfbGbyfpwwCp3DlGlg71TDyS9nHvUZACbRn1ovMjGUAUVUWZh+nDW0BRt9U75JQMXOyUSPln6U2U9Rj3aX7982vuvWERRMm2k1SDi3ZC5u1IbrWkX72/Jowy7s3MvYwi5Y0fWc2EpBLXPzIHwY/HmHdmmsJf3u9+zHoMaa5yOHYvM9OJNiUl1f5z56Wp4/6at6tjG2ywb2a8x6XsqmXWpz6JpiQGvVX+4k+dA8NFB1y/YnOwsPaDi3R5tcZ6IdcO4LS/gUl4Aip1zg3KKcyyYm89mkOpNtf7+3BK59GlQQXcviXcPJwohNVQBKEQVn20xpbFvfl7IPHltqIbUvCCH8ifn/PyaE8CnqOg+x2LsBfG8I4buAUnXiKomrt3Q7FfUnLVMuL1hP727w3jf2NB3aKOcbnspEe2Foy/kO4+nu/oA3KsKHh6otrvtTJC2qTtMr1XJtuwjObRl1iOhGOxqq4FbZ2LYMhV02stE/N1xFpN3BMqwMCCWzxhyFfox4zSLuEBqnWsLIqXFJjjM2eQ+zJ0BQBFWZQB0ObdWhesa+Ooi+CHf+RluNSYwc+xEvWAatcagj9zoNE95Jfmt7DPbvZHC5DNV7encDdPtrmdPo0YZN8OebdWwGvJmS3jicztwPnYPjiNeYIyLWGCB/i4EQFGlW7XMkru5ubppEWQvxmFAMSH3oDL6lP1ye8/yJIcFTjIllO48EsdEJQ7skJ3z/aU9PdBJO1hLcsAx9BbeWx3mg0WOjPGNOsHCyWJA1XR/xlGqKc3I5+fsLJ7tf3k8hbzakeSNLdkSmn7GXp0DshUGYYcSBINmOyqbtdRCDIwlUQIwniqRNSdAmup5cBcHXPXhqBKJXOPcNWsxi6t08tqX9t0j6aZ8J4CsAvA/A/wjgd7KLPE/1ZwD8ZQD3eACJq7dDWzYsa8FRNY5Oun9GRpP655EpVQNX9qfgz617PXgm2mFApfM8cgUcGvwQLQdDnOTjc92OVfPM63KXaKWoeTYz1Y6aaGWUqmfOJaYmkz1S1Yvl/jnxiz0+rwamqUEqszi6HkwhI7Lx0cpGe4jJ8tja17NMtO0obLKojm88Q3XrtjjjJItmZWptUkPOzt0KiCzwXOM5SAegMXdlBokc92jDrvru3MDzBGE05FgHH+kcF88in8OkhM4hDfMweEu01oE5Pjw7W/alHG0WpFnP4fvwkcwhmQ1zSwyp/vnzNOG+jsQAla10IKHOqXneBRonPocGj7oEc9JUpr5wsltPQBOD+vY4Rizm0Uy3M9EpAJf3H4sM0Nojj8Mo2PZHTgyn2LEdQRg7gZGQcor4y8okq/LMTqEAJOmf3kceGwDgd8UY/x0ARwCIMf4ykt9Lm8xExxi//Pqxvb1aCW06No4zXVaggHcoOLfKRMuaaWcm2uEgMekX4Hq5KECTHJX3fcezu/XeZ2rbnoapmd06t9UkOKoxrT8lQZUdFJsYRMHBeM2zqhlTtZvKAOzHaGZ6jw7j+yTqMTuyCZ7DYdBCF7RrksvruQGjDMBujNwhmKPgDGpP68XGCTehDfdmxrNyEFv3aTWd6W+zG+dMbbNPw7keZoPn/jbsMsPL72kycGukRGu9LdfyllvP2Lk9hDAKxgnM9YDPuJMN2FnEcjzUURDZIhWoA1Lt6f1toLBcgGeZWTBk7ec6p9DTxzoeJ7GYi8G7fc40kxcpmbtyjpVTx4t+03BuEWi8UOfZrFktrpeZQoFUymts69NjxJOAZp0/9RPeKZBaACkZEmtBWkNv5PvL32pd8nEa+ftTxGtqL1iJu3qDGHTELdHp1pnoiNdIKdokatpPxPbK42e/DfBobKtMtCAWOxxwamBbl1ILERB8JBaTrQ8h3GBeGkMIHwE0wcSbJp3ouaMvA/BbADzNf48xfubFQ32LtzUTXUQ2y+MLE2LODrXr/NKiY8M3JPRJkoT4UqTMAZqmtHn/WhCLlePeQEMrS/rUj3jy4gntR0GDbSfaHw7IMEqnDz3XBVsbgcgUZ/bN+T1ZkF1rkxymSB2xbmjUjBU3OQ4jXiUapUcSiBmmtMmZcKh+dWJZ//l7rn/BGCMGUlO9dRT377esiT4NI0Ko5+uZpDz19SIKnuS3iAHRx408VG0g9QN3wvvBrmVlAYC6louvJnH5Rut27PkmfxpGPL29R02cwdaXboi4OYRdHfbG2OvSuOpjTWe4MOJbQbmlz6otGZq7G3xg+evaAYNz245Heb0OZHTj2AiONX539Y7LfSmv88pRuXvGGZDTWAVBD4G9euudWf0ksDotSnvdk32r5+Z+P+elLnnMmp27lc1M93IrBNxchmbZ1f0bx+1smxOuvMyx/TNUSKRtTXl9/wl3Jtw6lfM8vzf2r5hI2V4pju/314hX6P404p7CvfdrYGwQPNqOFucSKBmem5lsYRN2VzrRp2HCU1ETztewAa8wzXay/yvS1cyebT3bYZwwTDYxqdeJVTXNT+8Oi5237uGxyWdTfn81sZhN2veYiRbtqwF8G4CPDCH8ZwA+H8B/pC7ymPffBOD/AfBxAL4cwM8C+KGLh/lWbyHgNEyICESTb500Lafl2ITurP+l2b35glhmdD1QYw8xTute5+oke8Zxd7AdvWVMRdaoKYFCMgOJzOdhxn3qbYhya3A15GgTeBEL4WajaPxolW1NOsocNkozOwSOmOFOVpYkGwCWTrAn48ky0YPI4q/Mn+2g10k48TrLorNeSrpGGfNP2fcuWd9jVTO9Z/ZujX1jGDeIkOrWG8RiiwFv1T0bGp5sfemGselorY7y3mBmWc5mpitk4rNE3Mbup/Rb9+9nJTpThHwRQUJJFZ8BsH/HpaHWLczaTkRSkzRM9zHIspFyrDbkm8kJ5bGu/ZDjy3sh35IDzu3JMrMgIZC+QVXCtXXC1uMrMdhla6xE0zhlNS8lzlJomjIwbkmjKZQEQ1qVcO+2EyrUJXqtPgGkPYTJtimkWrkObOavkLDqchnFoR0o7B2a6YzY7djbCgXL9ZT9OtL7M94QJRHJyB2zPW8eRxv5srEfvMRiphM/w7lv25r2rXKsLcrkkVhMtRDCAal0+csA/GkkLrDPizF+i7rWQyz2rhjj14cQ/nCM8fsAfF8I4fuuGvFbvG2gOQ07b13QLjPKvRJXTM7i/iaYbL+t/i6plXpwiSsB2Vrvq6RAbBbT/Oy8dcx8HKObAX2a5V3SRrQ/3gsjNh9nNc+3hKG5HxUL8OXEHEfhhOYoeAuyCxQSUKIeUELhRc3R/e0BU+Mn5k3Ylu/wZVmYjjDPzo2UFEcZ6Yo5dpgNJKq524iinwvnzsGMXT8iE300SHloJnp2xmo5Kwa9Y2vZcZjwelEiUl93qZJCR7gK1DoPAN2kHW3FLFw7lK19oROGXu6HBdrccO47ZixrCLXFxFwiNE79iIC5fKUR2/FA3HPwxCJbAtYsl8xEO/Y2ug/LQOG8Bt3dYAfpgCMwvyhjXFcS42HXbq1DZWC8fb2GU7N30Cm0j9j/ehGEPg0jDah7eC0AmwenREtZzw8g72/g6hvdwCWkFJpKw/Enc38G5iCK6N92koUTK9A8OsDDa5Lzs7eCAKXE5Qda5JNkD96M765dBvOYidYtxjiFEP5sjPHTkJLG7uaxfjJ55btDCL8vhPDbAHz0uYN8O7WjMJr7wTacAD+0SssJkEi9sx56EFlEtoBYeqyXNqs2sm6aSdGO+nuIa7yN1cL92N97D95XMLMPC2SHE38oOBfLxioDl9fN8kg9M2IUXHcxri/MkuRv1JbvylA3y4DgBnMn5psiFvOQ+nADhNfjJbizgMiKTLTJ5E/gs8rwrptVE12vIWUWJOvbtr4di1QMyM/MvqblZB3Je2JODjPw9LefruUQWxIAGXiWBbCz8st9HBDpbIgqQ5g7GtqYSwSG3FkCFMya9wFkmcMb4jTo+yjDfzNecV4ej9UypNQMjDt5VJTMniZHvIzwzVOCpkoFVGBcS0xxRIf6dq3fFpEC4CrTzGwXJXOk9hhV1uFh31bEnCpwxXk99PNRnCqMW+ZU2TdlwKwT9rZXclbCuYWTq1AcJlpp4mvIWprCbfRHdm7Z/lII4V8MCupaNU8m+j8NIbwO4N8F8DUAXgPwRy4Y4Fu/FcLswDVRWR1Vpgym9ca9q+EbJdyt7kuSmDUynEeSKbqkeZ1/RYBDSSIKSOcwXTf+Rfag6ua9b/T4r//3n8KHf+SI/+LfSuz5w8QX+kFkQ1f2TWasEyNhsnWa0/URz+55raItsaSycXkTUwbEZcRdysBYSFVuQitJoyPRol5NsfjKLMo44u6p+J4pFG7Ek1ubZLInWZYEgwxNVtzzicVUTXQjA0qCR/m5NgnRxhHPiOP/5PZmpwbCIKl7YrqtvMylAc9+sB0xz/PNJSAWa3HuhxqgIpMCrIYo5ZIQZQcrOeJ6n7p+T9XvL86A+D0SYt2LwIIr4z0b/rElkpXvM7/D4l61iZaDgEoCEFDEUun9eCGfZVNon9Mw4RBAdXoBbpNw8kMF99VO2PPntjmr4NzDlAOJ7TXKZHee5REByJrf5OQ0NIhwRraT7O8AQXvNxKH2+ysUIIxM9vObwx7nna/vOfmsQmN4mP9zTXu9ZuT+7ww0VTekZ65KN5r3j9EdYLq7PTSx/ksm+nBYyne29xdIPZEgUUg35aQ/tqV9KYDnAIYQwhHpbcYY42vsIs9T/eUY46/EGH88xvh7Yoz/BIBfun68b92m2IT70ffR25GxxMJpbkgFZLF1RqrP8QVbtHQBy948dCbagAxXzyHVR9lZS0YSoZ79Oc2qRfzA/Mz+/q+s3O29yCipLI7OZHN2ymHkdYTDODUDJblRCSBRCymdXMVcKoJWisG4E1kUdb1ivzwWsM/mfBT1kN0Q6XzthLwKC3AAud7NzqSq4IBfJ9rKRNtBksV4bDjYzEHt+qkNQa8Mii1BSzLUW585C3QwjdBWELKsOe/H6Mg+ca4CFvzK/bg0k6nGsycTPZqIBsDHJN5N9rMsx6ocYE9NtFRoAGf49pQXeQIUKlANlCUl9e/Kdfm+TKQZqHRkslnmPgf2rXIh6SQ9ABxYOmEy03zZ8RwA15lyzmvB+sjjt4JlqlQiZ4IZ8oIhzbqiJrrVxXHQSIBziOF29+95YIzxhqyZ6JLzpNbYJuVWEvnJA1T+mujLUATSiRYBrMeWWozxRYzxEGO8jzG+Nv8/daABXyb6awD8dsffHtvcFif2QvjIcY6kWxtSyjxtr62NQYBHrs7ORAsjsc3O/bBOtAeml+Gf5oLncHiAh4Fzn4YJL24PO5mztT5pvccgIDtdkYnoGs6Iilb2U9upKK+nNdFTlJIxTJ4EYAECbgAoJ1V97xLqJur5OmEIqyyPNPAcbPJbwrk6e8edpGM/4vUXKktwvvF7LkzMronOxGL2GtL6to4sczxGk2Xb6k/9Vvb9KOmep7c3zQAKk8ZRwSOAy+IB63rI5en0mufJZihHZUWkkN8jai87st/kduxHPBeBBUXWtzyTQ4BJYFYa/sS5BPLawYNIPNDFn11PvuvtOEKt7LY5ztbgRWveCDIl5mf2rLiTpVAOzwQxFVfcsOdJnLPJVpB4OW7tL8N+/2ox+N83yAy3x0mmXvFmiCB7N/D9/zSqAJgIxDmCIK89bXNKABqO72EHN0uSduzV7eMqWKw4Ue5uA7rGFNfs3T7mdev9KYktL5fQ272FEH536+8xxu9n15mzOoTwaQA+HcBHhBC+tDj0Gh5ERfet26QTO064OQQamVJ6nHlSUDIgk517pNCjVl+XMIHnyf1QATCW8cltgX+aRGi+BdMr/8XaqU/SJrUTnZ26EmK5OsEq0tyGhC3oBuJIsuzNME60bm+YbEjoKhHBg0ZP7tpwp35jaO6bfGfCiR0EKZuSzVFOeKrXIhqXKsou6sFOvdAoHSY8fYUZGNzAZIzIPAN7HkysFfwp+2kF9o7UwbaNh24ccfeEZa/b8HSWbXhye9MmiiRBDA+r7qXasECWxePOBcBrpl3Z3QVN0r5XjNFE3ixjFTI8+Zz7p/xbvlHyVSKolM9hToMPzs0Nf0AbuLkfQAUo+LPrBNpnDQrwgBhDaO15F7ZEbSoIc03N82kY8Q6D2C8dt/tPihvr+OovJ+vNM/nHGAnfSEXqacH1ea2/4MUQxJ+K+V6RC3Z9xCuUEyGaNceAb49TaAwmkSnRNCRTvQTJDZtXSyz6AlR3N+39QSHlZL37tDrp7HoW5H8kFXO1f6/476cAPgXAjwD4THYRy0TfA3h1PudF8ff3IulnPTajHVUmWjIR6g2FR21no54QHbwmMrplXwAnBAEMttuBO2bnttMw4R1O+JwyTKWm7wOM+zhMuJudm43cxJLZXBfFdSO2a5oAstDOcLq9Hmy+XmgFj7ymqR9tiYmyHq95fHFy1+Mt3fQnd4dmRZqfuMv47QIq35kyQ/m4IgJUQS0+X8tIcesX9CJLcBonvEahhKJebYx43nA4AZ4hUqUedbNqolvyPOX9AYO4kBwrjcatXBnpT7BsP7k7oM1sbBuIKovRO7LYSnqOERaVcoZWY2RruXUKcii0WIFiDSPfspa44sb4cg4JKuVzWjXz5XGA7wNpbuj7AMKJdpRFrJlou7ae3WfZ90QZk/z+jXYUpHLtEqt1Zh4dmUz23lkmc3GSDQlDhT7LASQVZFWcHmx+yUy9eD6tbHjZulHDtV8n5VppD1LvVxCLsT2sn/Cu59zmpTrPYzSRBAoJdyzRQsb+DbBnO0tcEeQooJFsFjloJo+07fkRtxQF8piJ9rQY479Q/n8I4dcD+Ep1nelEF3JW3xBj/DtzpwcAr8YY33vleN/STcI7HSQa3Oj21gfZRqu/Jnp1YGjWu2nkWnVA7XGpdhomPHmWndKij7jdjAGWafZp/q4R+8vGmu+11qVv6yCB7aa9bMTGQpmNJJN8YmantuBg/bBmkpsSWmPEk6fr8RqONlRwqvJwewPf1xypmuj7m5vCRymuF7WUaya5GF8xwF5k0RRZkcyUN+bzVmtZzcftfK6f/ZrptEhvhIEp1hMG1Tv1jnpf473XLT1nRlBmk4e166WLtafxzJgsVzPrbTgKC0P47U3TiWYG/mnk386eeXz73QB74205Y4aZMqfUExRcMtG0JtpX1lM7ImFT/+3QvZ4mvKqcKQdpmDxnGPGagIQDdVAgzv/MdcgGYqypEcscUEcmujOdpPT3MhCZ17OyrSVmhqE/ZK1zHSgEqj0kxuRE0eep4NyOmtpyjRTH676BEs5bZVElszh3ghQpqAex46npVkFwBgfvh5jWQ4MYrBNynL0ofzj2IpM820Lm8RplGPfvyNaBFqVwiz1ekPttxp7XpeL7i+scUkEuVbN8bNoXpc06UqLgfkYBHKx6dhnA0sG+x9ZsPw/gE9VJnproPx1C+LeROIZ/BMDrIYSvijH+l1cO8C3blBPdDVoTT0OfFIkDd8IZZG7TVyMbUq5vNVRle6wFAbu8nfoR9y8OCIQ028t8bm3IZVYzGhuOt1lkHa26PiV7tuhEE2OcQimnCa/cr9O9VVfL3lWqic7EItvfVBrHbZ1CxSy6GojZR4nVJpOPt1pZ00k1ts90AMrr6yx/nd2k83kxIG1mVms+T1NMARIFBxdQN5UlsPRDl7WkMRc8tZxlSzXRxFFu/EbmYGwi/BVBcmegYFgdt+UUyRKRYcKLp+2tVGWTe/LuXcRiIyedW0lveBbp7tbOZAAOmTdHxtulEy3YuT0lPR7OD80WvP09rSdzHMad41A/w5Wd25GJpkigda610SqrzdF0olVg3+HEpXdvBwJfVH3HuD43lck+DYJdWzrhRENbZILzmmAirZxB1kuZtfM5zNFR3z0rCwGQap4pXDzijtiEqSbcVniobdJdIFgg3U6F/WFlg1mAB2CcJvw4hXOHUOxzbWLQkn27GSReNNZ5gMoMgEzaSdb16o9wbtVCCF+DdYE7APhkAD+qrvN4Up8wZ54/D8B3AvgYAL//wnG+LVqSz7AnxXGwGVmBZBxKTT0JbVIL8sMSi7UmucfgOad5ajs09Lc2/NtGz0PAua3xdguT9npsgUTfWxt1i511m+Wx4ETpesE+KnQu+3Ey+29lireZYF7PV0LVW3e45hvc3P+Wb4IW3C0b3CEEQ89XZYKVAWkbUJ7azJ7cPyLOwSzuhF+iYb04iI2x/9wvv4F/4xt/CD/x7hW01M2wytY9AK41n7+tWDz/5bu42ZcBWCRXTNPTeg8SzkfWY5XB7cds/LP67cvePWDX5G6QEqUDZREbVXO4ft75Prc3YdPHZh0YUh0yY4nd6sm2A5Db39IyWmcEVMgYoPacXZ0mm8+DQnCF4wkUkokOJAAlVhJr4BrosQMyKdvlyzQDFeRZ2hRTEXCw+Aa4oc+CuMyWGKdUc2xdLzlqrjzei0z0suY0FAbKe1xDvMbKQgBIHWxFPKbYy8tsZ9MJdgVB2scXcsQLNcpVploRe6mypWz/mHDuubzSlBcTpGoejW4FtX9IO/wt3H4YKVH8IwD+KoB/P8b4BeoiTyb6LoRwh+REf22MsQ8hXJeie4u3k4CcdQ5NPAX35kyVDmiQkzjLTyzWMj7ncbQTcGe3BVraEvNdzuH1JYrx1uO0eNtpaMMKu8WwbTgEtCb6YDqCioF3EJnqbpjwDsORG6eJEqt0yzciMgFik7u/2WcU0/Wj0BgVRk4BqW1laTZwrMa3mgx3rkGqmGGt702xyXt0cRnDs4Kyp3NsRuSa8CU64MYA8D0/+QsAgO/68XfjN//G37yM07oH0K6VXevVWg726qCUS0ImurMCe4CRic7rVdwjagBmROmaaPvb37Lib1QWHIzYSeKKB2SBAvVilOTIzG3PdaL3mdv9jZQhns9RGWLtuGouDpm9GSYgcIi7h1hMvf/cD8AJ5BT7siKWPM1OMNNpzu/WcoLT+NowMG3IT4IYjAcaWaBKBWkV+7yEcwvOjcGUH9tez4nFuARUIoPlTrIiJrs3AvTADOe+sOY4j4+uQyqQTII0qqbZjSQQcH1JLHZ3wHCy7Qfr/urdKbi1VBIYJzwTmehHOLduMcZvvOQ6jxP93wH4WaS09veHEH4DErnYYzNahsfZxxUbL3eyj71iqhQLWj9KqajyXOAypzM5vfw+/8MP/Cw+5h8e8BkfoceSnpv9u9M5vO7uWCyIzeOCxfSclshWWrWc+8ztAtcjGa2bww3VMqYSFRM3Gpkjl2u+bNIyX82QCVVvPI/N9Q4iPoCwcxfja0IdxwzXbl//d/7BB5BECaz76/KLEkpfNg0V5gYCwJ24NoKhHoNdr5YIXwRhToPAq5UZMyWuyDNY7sGO3Wyd6H6MmABj7pH662zo9/Xf1zWw9QuYAdnNTqGt72pfexTBI2DORFM2ax0UVOUAQEG+Jx0Nns1i+1KWElKZdQnnFvWXebyMkG1xOgnEXTk++RxA1KQ7iMXUOuEpOTin5nh/fMTrz+7QjHJi3uuesqw9l/GTPC/kWasAAyMgBIDTyJ1kJaum1BssMsMtGkTVDPOa427i32I3RNw940Ea+/dx9vI8Puv+a6D4suulzrJECvCSFm+i6OntDd5/2kfZ1fdx7CfcS2JRZrvxBEhGvprHhxEf9tyG4r/dWwjhx9CuUwkAYozxk9j10omOMX41gK8u/vR3Qgi/56xRvs3aqecOiycT/ZqSc1D1JXcHMF1KBi2qzwV0Ldz6e7eZKub8AMD3/uQv4j1/44jP+Cf1WI79iCc39kbsG+8emhOqMQfY0N5zmkXg1mLnLp2Olo6nYq0dRJZHZYEYO3dLkqt13I4kczK3JYvCDECR6QWsQ/DaRwAAIABJREFUTTQupDxP7w54X603hoQMYBqnrz27ozJtahM8DRM+zGAL9gepjCwBIoWD5dIBJZNky2fYGYQM9W7WwTdgxJtMdGVAAhaaRWeib6tjHtm9NnR8NvR3TvRqZH1gdxXPJDB9WgDUuPTAhQdhnK4BLAH3uzuAkcKVdYHsOIWMjpHuO1lKSO2N73DIV0knmhAVuftwlRd55KsciINhAhDMjNaiIECCHOYaEKNewxwSVXcHHgTg6Dr7/uMUMUx2kNcjQQjomlglU6eQWJLbYCmZaJ/zOrH5jmYwMy5j4EEY7mgxuLcnQJaynTyIbh3P8niKOPRSJEEOJNvvf7VP8n8Xg9uQ6zb7HyccAma4doM7JJeXNFv+7cS2I+VWAGCVLuWmAlSPDf/8NRdLJzqE8A8B+FMAfl2M8feGED4BwKcB+PprbvxWbqdhwqv0o494+vS6qDCFhyz1J4YT3Wv4XnkvwJG5bUEn+wmvPiSx2Fz/t3MyCyOe1VfmPgCHxNWVcO4cfW1l/FtjWDaa1u9DiiQzQ7gfJwq3SpukFSmedSxVJppKXB2kI/jk9oCpYacrZnKFzMj93xrPpyTlaY6fPJt0fMJHfdgr9P5K41LBYJUBZhlAwxQxRdu5SfIYQRCX5U36vFpGZli3ajizMdO6x71RprAaMO151CpvYOUD2TmylAae3N0Ab1R/L2DVLSeaOSBKXqQfJ7xGvg3lpPXTRFmRl++HIGuOfS7v0U70dcRiXm1mfg6DpQ7jhGHi5GS5H8krIrLMqk44naPh3B6puOQkh8aY0oesZXwuJ+bKx1XJiqyZvZDnRSKZFJxbBHg6mekUEoqSud6n1S7JZAXxnJKoVEF0Tdxlw70pd8YFZK+t67WTbyMJKEdRv9peOyca+v11omb56CrfVOsm52Zg7P+espO3c8vqU5c2j6fwDQC+G8Cvm///bwL4I9fc9K3ejkT4HdCGlXQaRHaDLbh5wXPXRKsa42HCzSE06fePw+jOeKuWjSNvDZpasE2nJhuDV8K5WQaoFdnOkDJbwkpEmsVCnmUSrGPp3vy4raPM625lvfcUcXsIJuT1JJhHVwi+7eQfQiA6oIKUbeIcAp4szeUGBjfwVIBAMZOv+r4EBcAcROudNwzLziCvYwY2M2Asw3OpA27MPeYcWeuuCiRSqOkgMtEsSCEYe4GSmKzdVD3geh/NZs1kWDyOAqtrBQr0QvV7avUEynQtnMnyXvUzqev9VcmTD859BrEY+d2dWCe6McHczTVU2hyXr2Hp+OU2Sb7eDHKO/LcPk7ZRAJ1pNoPEPc9kZmIxa/xLIOVC4jQAknhNwaWVfdANNnGoCsT1Ywrk2kiAMzP11v3NmmOBFBAJhpND3o2V5PzU//c+U34KKJNa9v3Vu9XEYpcHaN7uLYTwf8z/fl8I4b3FP+8LIcjSZc+T/fAY4zdjTmvGGAdY7BKPDUCGV3DDiUZ1HfVL+nq+4J3Nzi0gnc1jYmOeWmlJoymW23U8vqgmW7APAbi2JNpixbXGqCBHinAnEYu1j08xYiAyCYujZRiN/eJoGdcP3Ak/DlwHsZObwL62vkVwZcO5eCS369tSZLkNDtkdCYV0s8VXY8u/zaqpzfXklhOt6gFFpoxl2xhhyalhOA5Du/aWOXFHEhSzrluM5ga8lAcF2r+HGYHTFGfJQrtP6kRPUdSxCid6iC6daBYk8twnk9eV2Zytc8sNXUCvYcfK2WmVV6T6QocTfQaxWBsBwSCYK3mdN6ir1jdA6EQ3FBA2x4eoZfakQsB16DhOPGU/q2FMSCiNauFr3MXEYmL/8OwvaXwiECiZ3gXcnZbk8D2IKTDEGNGRutvl+UmbitcUswAQQILwyx7F+2e2E9vfpdydmDuvP7vDG40Mdm5HESA5iv5XpJgxvkEE+YW/8HZvMcZ/av73ixjja8U/L2KMNhnO3DyuwvtDCO/CvGeGED4VwK9cNeq3eGtGhsoaQBbVnTPFSjNQS1xZNb8sY7B3apkzmPoTmRQKZfTHYlZDtrFYFM+2Bf1Kvz+do0hcFiZo98iAZh1M3ngbv78fEjlF+Y0Mc6Z4H+2M8zVioR1siYphKoyAhsYQZdeOcTESTJ1MSSzGmWFTTe4BFpy0q52bqpuVfMuORLNNrBMwUyX/5YKGChjs+cR9cy2cklCauDavp/yBSY+tvyvsjgHAk2Jzt/SumRGxZoH3z8+6jtc9V+8qboMxT25vdhotzAj0kNJYDvYwjrPzcGPIwmgYXj9Gl1PJjEh+n/k7q7TGd3rIjoy3Ig07me9tu39xlmPyPc/vepqiXBO006i5BvJ5NwcbBVP2xWVurG+wqIkVWvBXwYn73H+5Rq//fexLOHBEvZYzm0WRfdqBwHoNvJBYTDnZgnislGhs96/RERJOLxIS/RgFnNtGR3YiSK7g3F6+BBv950NjWQG61L/NHs4CCOk4D1AcVZB8mPCx71rLvWrxIhVsU0H4Ogmw2PPzeqaSdp6yk8e2thDCR4YQPib/o873PNkvBfDtAH5jCOGvAPgLAL7kynG+pZuKDO2cgvp6EpVcSRgu2xDXDcVPLMagLIxQoiQ0aOrD9u2a7fY4NCyuPI8x3gKKxfT6BYcFKzLR1U1xrJ9ETfMU6TfVT3a0dclyEzgWQGrOZkfMikRLDVMRZU8G3jVw6bTJWDqMTMIpXc/LG1Sk+thzA1YFmgDNumsZEArOnRmxlYZpy0BS2TaKQhn3v6s3niMzIjrybq3rmNF3JAFK6/cwI7DlYNcZWqmPLgIYVpumhDDxEItxqSbuUALASbHrCkcE0MEsDxQ7rQU84wn44NOqTtlHBqaJxSR6yiFl1s1rmLUPd+N1JWAs0BdjdNR12ookSsavRfZZNgXnXmqizw5E1scVO7e9vwA2kmtF8ylHStl0LMjCbUYGCVZrxPJ8xHGFdjqHP6PZv3p/pGSJSkSy/T0El/2h1ptr9OYlnFuyc+uyk8cGhBA+J4TwUwB+BsD3IalSfZe6Tj7ZGONfA/AZAD4dwB8E8FtijH/9qtG+xdvJkDbKrRNSH21NvTQJs9yANNoFEdg5xGI6Smcbpfe3N2YG0tKNbfYlGBLXe7IFPciosIcR19PYOLLhXTYX5EjAtUzii8nriHFjnxOL8U2QR8kn3KmaISrR5KgXpwYm36R+4X0nmUGzfp8Kennrxcx3lx0808Dc9x8byJTW8/FkGJSsywZtMbWhuBQWTr5ra64yNl4rQMkMfZaJXkhp7m6q7GxY7scYlSMC5WfYOufbb6CbHE7nBuoY0AIvKx3TNBalA8sdESAF86Q2M7hjehSyiS4mYWedspKhTGPle4WHGTcF8WwHGdCKHp3QCT4Jm0OVgDHW9DgzDLNMIHAF2adwwnoBdV8D2lZNrdKR1sRSt4eAG1KPzu6fzuFOcC6n2P59OwYZRDDsCxUk0DrbnM9FJS40u7kgFhPj64eJlploUj2ODC15L+o1Oo1PO9nMvulZPXsIOBJm9Vwqodb3xwYA+JMAPhXA34wxfhyAzwLwV9RF0lsIIdwA+OfmDj8bwJeEEL70urG+tVuKynL4nDUplaaeSzKDssVqY6c+X0tfnFdjmFtePIntsOkLsOti6vPYhkIz6w6SHU9jznoeY9kYezaQoZCs3tC+XtXFKjj2UhMt4NyMgVrVa2kDQ2SSaSSXZ/k7AnUD0pz85Q+09VGBnN20HCWehfASi+Xvvo5Hqf6HKktTX8+i/Mqp4XM/jbvsth9j8xticLkaplpneVlNtCWZ1VqTWpnz5RgJ4K3yKEaQhAQhVYZNBo88GUzHeq8yJWmsPlIwzc7N90VAyXHtgyqboJDjmXjqpo89R6eofWY9T0PyPUiAbrRr54HsZHOnXwcNlM1h7Q/JybadZD5HJC/ExNe4fhT9LwE9Bfcu6v3j/rj1bQ+E2XrbP3GSyTdQf6+tnERPvo+VLFUFai9zwmWmuBEoLufsuoeJ8UukgY0UkAgZhTRTpHCif8ZboVAvat08NkgSl2OiVOKxbVofY/wHAA4hhEOM8XsAfLK6SEpcAfgOAEcAPwZLM+mxbdqxt432cTJYpuc1xXJo8qLj2bzphihIGuqmYW1MR5YznHajNrzKcQDaaGlJfWw3RM0S+hCZ6KMjw1e2YeKQo36MuH8iopUiUq9qohTs9FJiFQU1VMyhp4GzsrvgUCILc/uUv/Pf9JGvNv+ug14+qJqNHOEGioIyKnZulrlTDv5xGPHO5/ftfvsJterpMKZMdP31s/e3alE37m+xaRNIr+X4M4e0liBqEWqxQMO7nlsZGC3No9ZewNZXz32EADNLls/xsHOrdRMQmWhD8q/uw3ImMqqD/d4th0ebc0PxfORz7gX8FuCIsHQeDyTnvjSBnEbbUCe7FQQp90VH4N3eH3zEXecQT22YyXuugT00MtHlnr/uxaFpwS5O5Lz/7ur9+3zcRnIpJwfg7NFp/JftIUAKotjkhvtMbitIYAWNvHBuFYTJ86C+y8qfwfuXcHMiUcYlZ1XNs7YZ74n9cOrtUod8PUMZtIIA9R6kUAKPEleu9p4QwqsAvh/AN4UQfgHAoC7yONEfHWP8pGtH93ZqKRNdOcGLk7xlMt4vKNWCY2SOdM00h9aU0lOxVbBc3O8Sp7Mfs/bwdRmB+lyW4c/nLZnmxu+SmXUBm/Y2lonOjv5K+BZnnUbObMtky1i2NQcrLAM2GyHW8bVm2thkx4lLRAj2yW6Y8JxIoXXDIJ1wdnwYJ9zd2EtdN+wDGOWnM4ys7p9D2dYsZk3KMx/P810YQOdoYNcbMEA4Aho10UvATpGeMed33DvR/ZQ0fo/1ucOE157VZ6dWZ2hCRQT24un+vTIimtMw4sMajv9Krjdn/KvxAfVzSONQmWhe761YbTUKCGjN20qq6fbQZLrOTTFRA2mOMUjgaZgQoNE02/V7e89jn+wVm4W3vaa23lUyWttOtKqhzOek+7TzBqW2LGuKdXk9Z99PqLJ1fA3dQzY3jpIDUioVBMxAEQ/SqnXkSJAeQMneLJzQxaZqO8F3h4PhRKdMnjVHTmOSQLrJTnZllPWjtnPubhjcu/188utTNcN5DOr9WZlsVQLBj0cHHLs43piSkl1dwbnF8W6MuGNOLkOCeDiIGhrpdZDiye26T4XKDlCIo6FIMtTfaE7K7ZzsuN4b0ASIb+cWQviYGOPfBfC5AN4A8EcB/KsAXgfwFep6z5P9rhDCZ181yrdZOxF46poZEvBP0+jl9Vwyqpk3rJu9sWidf4nclopOAmsmRml6pnN1vVw+b8vMXRtqjsz6g9REE4bgFpy7FW0sVmImDzNMWzhd/U4zsZj5zYiFdmHnNjPRvNZRsVt2w9SUIyqPK+ZSBedWxGHZuG8ZUsxJX5yoC8svVK3/kom2mNer+V4bSEuAxAEXNzMEBIpurmMN0sDeqNukMlrEgDGJxQhZ2rGqUc4zrKVrXY7BOlZnqZvjl7WA9rfF61w1OifVO6tMqINYzCGR9EQ464ODIRogwTpH0NWF1Fr2QPbcfOzcKhPN4PxlX6ofmYkW92FrmAq8L9ks4eRamULlBNfZsvrtq3myyPxdWMLVNZyg+noqkSiIq6QN1W+/JSuxYvUxThPGuEdrrehFVVO839+jON4an01eqWVF03FVgsj3UJs4jZdrSf16gdTx2DdMXqzc4+r9e5xi00nObUlwzL+vvl7tT48NAPA/A0CM8f0AviXGOMQYvzHG+NUzvJs2j7fwAwC+LYTwxjkC1G/nVsIr9lFL7rB4sj/seqarmsbGNf9a57MolmXorbBqXovqHQuV5iqeMWPfTf04pCQeIGrHDL6cgSrbICDNPTGCVokKftwkpxKBnY1ElnGc1r5LuLUg/pLoASERodgtG3WWuU0xUkSFqufbzsf5+VfZ1HRcQPHMTHRGtvB6dlkv2Ojfo6lu1wK3Shba72HvKDdkp864/2o0tknCWpJd2yBd2F1zOyMtLGmnNI7WM7THr7R/1Vq1BCHLdxdbQUNeBuIhnjmNDi10sY4zYxAoM80K9svrCwFeIsRKbcp7eeS4PMzbmnxsX1NZExTZwaR0XifY0ynL/pVw4mX/UZlmmam8LDGQ9y8mM6fQdFwCSe/NnLhKS5YCDvZqo49h4ujGY5mJbzRJrCZQgF6ZRokkEDavXfc77tA2G9I1B6GhmuvXMN+zQHA3TpQoWHFarKSvl83dxwZgO2U+/tyLPU/2zwL4NACvnCNA/XZt45SMEjsqJrKCimRBQgD3tVplVHGtsXQSizki8pzRlkfoAB/JmWL43I73MiI0QDts3sac/tO4h/VKOPfQ1tgFPAstz/jLSP98vcnOPWg4m5Zw4EaIgnOz+2vj3SbmyAGES53o3TwwI8WXGRBKJsmtoUqJxViWmPe7hZa3SyUynK1V90wJd4y5vNbwhd0YrAwjmwNsDVRwfpZN1oRIulYvjZk5lb5aZlZnDOhAWKpVtscxzRkVvk745oIiHgO0TJbnHLVPsLGu52miSk/dNAuiAttvpYUGYEEBDyIiHbdQJxnOfZkhL4nFBh5symsgk0BSpGuc8E450Z797/JvSX3Teo3nQaMaAdJC7wG2Ey7vv6C1eFmLQkuxTPqSiW0hycj+nvq/ziYsFWrq+2dmdSa/BVzGWQIUfDcXzq3HBmDrItm1rUbzONE/BeDHIyucfWxLWz/qgNb7yEZ5uaBsap+8UdtLM9kOw6s+X2cBbaOUR3i3UJRmKwTlAWPcxaep4HOeqLCVFTunHTcZvu3v6/p9JjrrgDYFtZG0mu1Ida4B5ca6JGYR2cz9Jhbn4wP/RkYlUWUbeOOscckjxcpJ5zqKWYt42/Jv2wcgwiZT6oSqtb65GcrFrj8OiSXYgskq4jCtRTyatawtVtXtceYgbjPRuW6/9R4ZdPZIlAzKuR6q7HVANLPrrW+RybCk+1yWRWtlk9dax5ZzsP0dHnUDVZ7SMl7DUvduG7gbcielE+0IdAH2GpXGogJWeu/yyFepwNU0xTkwxwN7aizpPJ/ElSxTGqPxDcb5OIdrL3OsyRMieB1kppg4OTE61kiRiXXoRFPFjWV9ae+tp3HPYbO5v3DC+mGiOuyyLMOZyb20HIuVt+TxAZo4zGbnVk5w3gOt+3M01WmYcEPIERWPDUOa5ePUJvRkoi8MIC32vhlAUk50GTDef99eQt63efutGWUN4JPm/3ajrj3EYu8G8L0hhO8CcMp/jDF+1eVjfus2FRVdjlsLjoBfemrw0vHLFsTW+WoBacrCOGqiPZqeu3MVsZjQPJW/Ryy43rZqS+7H0jVIl5hMRkRMcG8hMWWjHzjcrhfoh1wTbRkKKsOkNEyzEdTsW8hnAemdvdogmFr6JzqKQJpzdgBC1AWLmual/IJkCRQpGwsyLfWAZgCEG5gtYrHlWOlYnnaHKdz42I9Awd/FtGZPwkm1SMcs4+VEvldr/jPOBW8m+o2KxzNL1+R3bxH6PLk77Ah3htFQcah+C2BL56Tx8fXu2DLQm4gAHgjz7BOA1srdjaUcqzODbJ2z1L9v1sN9QFOVWJTnPDXmxjpmn8TVq0+4KdYPI56+woIUmlgqvb892aySSpOB+8GXiVbv1cw0i/chs/SqXlzUPJ/GEa+wNXia8Mq9/f4UXNidyRVBbkUmq+HaPMhhsmM7ieNsJJwORD+5vbGJ3wbO+6DY0zuBOjmSmugYYxFkbawl4tv2Z6I5nFuiQB6JxcwWY7wqTe95sj8D4C8jmUQvin8eW6N1C/TVNmoBHVW83ElWbMGcqbl1P0Z6YkFdPERgqpa31Z9ycGXNs4R2PRSc2zYa88Ja1r11ZKFfAi9mTRGHayuJq6Vu1vpmZzi0tYkpJ1k9097jxF6QbcstwbnbYx/GCdPENEBnJ/Va+ZbWuwsBp36iBkCCYeo5xIIQ6tkBwN1tg+WaGEfKyavJ86xsRSZWYcRsbK17enezqx+1ahhTRi47NftrgHbw0iaWCVSHszZg6nEyQqTTaB9bx+wjFqPrdzNTYdV2q2xNWUJU1fQ65Ay9tZnLs27g/1f1ibUPK3hhZ4+0BFZNBpVbrGrSredWl1hdnomej5PgsbQZFuIvbjPYTix5b/Max66nayS0E86UKwCPzq+tbAE4aqIHHuiUNpTIFh7J2pTGF+lxTQymj98dAg6tehvo91NySrTa+vvs70/J+ClZPMV/YH4/IQjE1UzqKtYSS/1AZqoHXkonnehHYrGX3mQmOsb45Q990xDCOwD8eQCfiLSf/IEY41996Pt8MJqqP21pGpbNCw+1JVWcUeXbAPTNU7bnC3bXZKS1s0uAgnP7nej6d1kg66PKRPcjnrx4Qu7TJh46t7GNKTs+ZWOQZW9Ns4xWWjVNQ9KSZTXVVP+VQM0BvknlWknLiEm/PcgsGINj9SSLfxLPTtdEO6FsFO7LgzoMVtqPEw7k3XXjtJGza/UPGMEeAlmVNaHVBF2yTVWgRhGrHPu2AzuME6Y5s10vY6cZAr+7ZoqYou0op9/Trtm263TJM3JnIW7QVdlMT81tRi2pGuHXiLyLIqIs+5HBVEHYmO7D+7g9HGxj3aHvvJEzaitcrZkxlRlljpkjgAHo55bO0Qze/TDiVZHNVOzXtpOi2a3T9WL/MeG2Yo30ZsKt/XFQiI08fw3Js37CnSBPZTbKoIgrhQ2lsoUKfSjRUmKPW4NXNhzeE/xicGxVzgXY8/oo0FiM2Es5ubl/iV4kJUWAjQZa1xoO17ZlEPP+zJNyNtSez93Hdn0znegQwtfGGL84hPAdaPgsMcbPueK+fw7A/xZj/PwQwj2AV67o60Oq2fWj2+MyE+2Ug8htr+2qiIRufE60I8p3kZENf3a57E853KdhwvPnHFrFjJq1dkwOadOyzERGsLLoazdMOwxIT6Ld6ptZvjnreGbXthiec6bZMF57xRwu4GoMDquYw9M3cqOzbcb3zmpxAR1F90b5XaQ8+9gJddCAzDxaZ7vW/+7n79l8d9LAnHAwvlM2h89l/VyMhcqh9yBr6P3vDvjV3b3aMG9mjLK6S5aFZUaKrvVcr33f7jdw4wxYCZ0UW/XTFxqJwPqIsxasRpt4EA+8zlhJDQFa4ur+1i5fKMfyIJlo5UQvWSx7QzkJIx7gNemZzFRpwV9a87wJMO3R4AsSSpMbOfpvtDyfrXfaTx70me1EH4cRd8/Y9RF3z4TElaHTm+9vlaTk44C22cwgRl5HFLHYxZnocROo2yM78v3PW8NzO/Z8zq48FkYQhPRvQdm378cOYo0z4srWSPcF2W0km7AvlDqHeHePElcvv7FM9L8G4IsB/JmHvGEI4TUAvxvAFwJAjLED0D3kPT6YTTk0gyACkjrRlZNdTy0FjTr2I0KwnZa6Mfjzpp4jtI1jBqNRtbqt/pTDrUk87ONxJkF5cusLMJTtK7/7J/HTv/Cr+Pr/8PPmcdhGZT82nGjiqK5wbDsSD9iZ5iFDgqyaagVXEzVF/Tjhyb3hxCJu5HGsDdj8bYt8x2WR4qyhrTYZVW+uyyscBmpjlZN6tP2Ep4IZlund9pN2bhQpytO7m53/r8pGdn0tsPPt+csmTwx87sS3A3hMn71lsHQk+8iCNHk9vYTdnH1bqlYu9a9JtDwaqABn5x6mFKJVY3n1FR6cBPj63TlglxDjUOt/6qcwfBu+rafW3EssdlzWJiMtDmcmerSzeb2A/h+XuWrZHCqTWDz3wonOjogiP9rWZIv+jePs+SgN7dMw4fVnd1g29XoP6q+Ec1f7Y0sG75p5LDPRwsleeS/EHmhloodpJxe4VTwQcG1HgE052U8ZkmCY8FygHKwgQCIuZfZ45hzhz45l0dP1PNOsbGzNh3MZXPyxXd+YE/23ACDG+H0PfM+PB/CLAP77EMJvBfAjAP7wLHS9tBDCFwH4IgC4v7/fdfKh2qTDM8NTzcwX04GOUdY4aJ3otKBZmav9+fYGRjNVDiKZhVWabGCbcTjGraBDDPqb4Z73t4ezneif/oVtPmwlS2rJWUXgbmXIHbP8i8pEm5BdLYPAYJL9mDN3NjM4Zbcmmd5xfqb1N5LJ/jUUfQRwKw1nJjGU+mffsA3HW9ivpeScFdQqrl+c6C27dzmXQ6gMlGHE6wLG2fztMbOLC2Kp3g6QLGvN3QFvbLuVhGr7+7QNxW1Gb680Yb1bZnhaREGtjFpoHcOK7MnHLOIntp6qQEPXzGLM782ZdU1jFpnZGzsL5slUrFrjKpDFEUDtsRbPmXyLaay+ZyId29JpaGVWxZqU78PKKICVWC6Np3Gj3Bd1MtZ10gzGiMD81WSkqixBPK+65jeEtPcta4nINHcCTeOR/3palXCVYjOnYVyDNwZ7+S15P/0C5zbYv69l53YSi6kgxTU10avt0WKA1jYXLwWJ9PqVuCsZZfVnchom3D9rZ6qz+oRJijpOAA7L/esvsB/bc2cJICmi4GWfMubGyAMoilk9JxnqIMd6f+7EP7brG3OiPyKE8KXWwSvYuW8B/HYAXxJj/MEQwp8D8McB/MdV/18H4OsA4Pnz528aeS3l8KTjNjz1ZGUnZiNNQ6PEhkkcjrpNU9ogVgchjWFhOS2jbNUb8mSqMlTW485TQ6MwYD2Zl92zqWW07gIWr+HCZo23pRS31gRZ5FeZjM4yYvJxu663RB7UjlrSMdxG0stRDo1IfHn9MNqbYA4qmY5EhnMf2v33A79+LSmorq/6r+FQqwHHo/CKw8CCSq5zRBuwlgRSup7X6w1CI7snMNB0/9GstyqN63o6tDLBTAXRkmNiBnyMJBNNDE+LjO1YBAVqn+bESL76Ce96bgdp7ACOMI7HCbeH0JRuUc8lnaNJIlk5TnkfliEeHCRn2REyjzeDWdvffRxHvC703IF5HWyURuRzVP3faQ6y3BxCk5xslxkN230v9/Hktiyj8LxDO1vE9skxxiaJX/oOgrvhS8keAAAgAElEQVQm1sxEq8C8k/hL60Tbc0hnKreJlK3+PF/jOunE8UyzIh5T5U4qsONGMyk4t1CQsOyLU89lFE/9iOesprgx57Z7mPF+Z0Okaxyv98B9EL7q/0U7U+1TJjmY90/HQxEAqlAGEs7dhtovTjhLmhXXK2UV6/t8lLh6+Y092RsAr2LLyP0Q7Nw/D+DnY4w/OP//tyI51W+JttYotBe01SHiUUdVn3SpLp2C1pRNSi9kR5lAJyl8r0GwZTVlaJTnqRrBS0iCzm3WeLNTCazZrrzQK4bq+lkujqbMRAs49mATb7Wur3tqBS62m5Cu8c9jr23aXhB7ZeIQ00nP3+GBb4LWs7e1eHPg5UoiwCtITQCSiZ6brFcfJklK0oYqn7c5W5s5I6XJUPyWAazg3NzxPo/DgaFx6HqyOB82DPMcWaB6bihCwGXsAsUBcOIZj9KCRZ5Uy0pRll1BYLS8owtJhspz1JwDqvW0uqVFqNnqhzrIsyY1e0dDtYbWa1jv5m0w9nFjjc5BMSYbBpQqG1Wgcrn/SL9TSUo3btFju98/OTKhwslm+x+Dcw9jqkfn362S2BLs3MIm7Ebx/ocJNyHghpBrKidfc54UaKoWnJ29n2oPq7+So0NeTzmxnLiUZfmVhrWCc+ea5rZ9o2zsjDK0MulrkOARzv3BaiwT/e4Y41c89A1jjH8/hPBzIYR/LMb4kwA+C8DfeOj7fLDaYvQTpmMAO5ba3DoR9c9RPQvWvNm4GwiyGj7Kmoqybeo9Kvizip7msXibp24MmKOqpB7dI8vzEAuOZby2AgeKmGX5pmZSpv1CrEsIaE3XFGktYoaDW60bJ7xT1n1yA8yq0c9BJ8voVSy59SZWN12TvXWC62fvJe1hJEbPiUbssbdrlgFnTbRi/yZjs6HK9uafeR9a59cZYuq8ErIvO7iRftOLm8P+XRUZtbo8vRvtuj4WeGTBPQXTpLJ2Aw+25nszQkBgJmXyOLekhEWVNOR+nmwcnfq4DrooYrGlJKCx35R9KJKuo9hLPHweikgtnaN/sydAodbQDOm8VPGjq9i5W7wVHG69XUNb1z+9tckPlQSi0vHthwlPKAO95kmhgSjCzq3svXS9L2jDHEGLdwEoaqJJpl8R+vF1hrOTq/nk46mxSzZP/YT75+p6vr+fHySfj48jWpnquv9Lk1rq/q2a7rJpnWhfGeSbrYUQ/lkkguobAH8+xvifG+d9PoBvAfA7Y4w//DLGwnaBl/nUvwTAN4UQ/jqATwbwp17ivX5N20LyZC66HBrV7VidtxBJSVJRQhab9T1cDmh7rib8sI6v42gt7DkD60fp0412U99kP586or+7h8eJJpDVslmGd34uZVuIWaxo6bJQ82yr7YhOlEjOYjNerp844U+C0ynSGzF2QZxxjjxKCUbvlvloM3+m/sPuWiA9+0DGr7IEahPTpDOlE9Sqr+dZnH7QOtRmgIE4Chto9NzSs4sbtEVuGTJaG5us7GPRUT6zJvo4tHVFmVPbCUfZKgFhknpmJjLzARAo/nve6MyxlnWyzLiNMWU57wmKZKm3bP6GXJ/dDuyWc4WRr6XjEwKiidAC0vNiwbpVvorXKlt7X9kPddoGh2PkCEb7atY1EoBnw2JRtyn2NVECxrJt7N3nTLoFB1ZZexUgVzZLLzS0JTpt4PtbN9r741EgmZb7N77JOpBFA3UkCCHRXpv93ahpFjJ59yTddlKyoo49zlcTTca324fWsQGzPV48/3yOCkCtexB3ghcZyeodqSBZRoGocjdJOnsof3/RvwOZ82ZrIYQbAP8NgN8L4BMA/CshhE9onPcCwB8C8IP1sYds7Ol+1su6aYzx/4ox/o4Y4yfFGD8vxvjLL+tev9ZNyg2R2jvAU9OroS2sfw/xyjl9WcdX6B3fnLxNwRKBlaDLhsbkrObaTyyWnGY9ecMYe/9pkFl0y8gqf3O+t3ehVMRjFM4t4NrKyebs3HYdosqirJFiYSBcCJlda555ttBmv+SbaHZyTXkOkbHSzofKRPNNchijYBlmmeg1i73LLtUOaXG8H6fNvAJQQEa31hh1bEmQjiEArOw6cxZOY/FbG9fR9dRJOle3flgJ+1YHZHXOAS7pd1rmpcWFoDVS9zDrfV99Q8t7936Fo+OSjdplzSuDVGiiA/V8a9cr7522/W8B6gDNeb833wfgpD5r3bx26tl3xPpQUpIyGybguL3DSTqbp2QzPr5GSmIx4qQrOHbm3LC+uYXU07i/B65/GkbcEHbrkwhCDBO3DzyZZgXXZrwLiVjscifZQ7zG7MjTMBFkZ7vUILd+5EEslQRYM9HC/jCCFEwVAihQHhezc/s5kN5E7VMA/HSM8W/P6k5/EcDnNs77kwC+EsDxZQ7G/DJjjL/0Mm/8Vm0eneh7lZlSOsYOKBqr0fDClRWzn6fGkEVAu4Yj+q0/8vP4pffvtYDURpz6E/Wz3t8jjKOv/Z6fxl/8oZ+j51jGdR7j9m/p2Zg10QJO2Y8xyZaZOpqcXXuYInWyBwIHH2PEQDPRqiaaf6+DcGIVeZOqGbJ0JHNTm+jRqAXN7eSIoutMNAtgCFIdYcB1rCbaAZNrPZe+ERzLGYEdOzchQqRkXwwFYxiF7FthwQ5mBLJMtFZKGM2sajaM3/nchjmqTLRaD9MYOBQU0OzPw8jLZMr78NICrRP9hGTk0jk6Q3y0MtGZ7EcYxuVY1H0AWz83jxfg2WqZLfMqCFjfsCQj1WvIVZlgYZN0Cm1D1rixIkdtjQ3QEkG3phOUM4Ec6q6CJDzQyo/3Y6p5ZhJTLHhlzoe5JbTS5XNO8jIIXg+NVBjNPXLlPOFlM0pi6lIdZ2WPKylDyc69kFPa796LPP0Qa7chhB8u/vmi4thHASgN8J+f/7a0EMJvA/DrY4z/y0sf6Mu+wdutrZTztlGuo7JMKkQsWI6a6WdebVeRPWBQtHIc1vKbF5DsXv/tX/xV/LFv+VH87n/0I/AXPrvuTxOLycyPMyuqjKP3vNGZkjfleFuMvqeWE70Qgyk4t/27bg82nC4xOHNHjG2S/WRvwgoir3WWxW+f2r+9ZihWzLJmzZDQ0EyR6kC+GeEkS41MbiAe+zbT9DI+CXWc8Kqqq33GUCvnz/2WE205lIx4rW/o18ZKpaD17NaM5lj93c5Ed4ahv8oU2UbKi6eW/BWXL0ms+Nx4o2v9OOKZw4lmczvrXCviwdZYYgFLT8f5dwzw+3TD1HQEVlk1H5mXrFWujHqrfvvu9mAqU5mOX9GXJxPtIR9TcG0Pug2Y19AG+EsR1GliLA/a5vJMdddHvPqCPR9bHaITz1dxYqzEVHyeKomj9PvayDsdSOXZxGF2pNj1ivjsHQLOzYLsp2HC8+fEZnXUvFucKuv1PEiiFCYsJJonQQHwABNAbGRnkN+8fhxxIE5yP0TxbWmOiA/RNsQYf4dxrPWxLlm6EMIBwH8F4Atfwrh27U0ZovhQbr1YlFMm+vKopMsoV5mxMzPRlpHOoGiuzHG/zU6/75gslvd8oJWJ1uPuiGENACcJzfExDveOWm7L8MiLatk6GW3kJEM+ODbLNPNNmLF7S9IbB3Mo4Ch/MNg7V1jvZXAolWlYN1F7k1RQNk2q0v4eM5SQwrknDrXrx6ihfJSUhBsnzZKFYn7s5JiqsRwJd0KbZHHLit4yENIzswNYFolZW49a1xuqZ2QyvxKY6KoNy5wLXmahsiD5HEU805oD5emKgHJzjoJz37TvUY6VNQUdXc9xZPA9tdfsPsKABnwSNJpB2Oco8m9Y8TYQm0XCla/UER4FnJnc38vrwoiZ0nG73ptfr4Mkqm5VffcpCC7mlcx0c/SjZucWNicLFIsgATuubL4lQWHyzXDiUhXAUqV4S5DF5LPhSS+1xiviUIVcfZO2nwfw64v//2gA/2/x/y8AfCKA7w0h/CyATwXw7SEEyym/qr3lnu4Ht8XkJN8ccCAkECwr2BnGXG56Q+KZsXMm1brBC6dz099q5CqB97zArf3ZRoda6MvrbRZLn7C9dKIdtdwmsVgBYc/kFt2kAy8Al3mS7NsCrs3g3t1g968itbqmKG9yRqbbyETX18t6QGVEmRIYab5am9xRRHq3WYQ20Z8iumMGTCccgmHUDgNjLlfSZK1vvDU/OiPgxuZ8V7zbPSEWyUT3bXgfk7CzgpdSHqhhIMYaFkwMMCXNoiDSKsOk+mA617nVDPXmWMW+xeo+83hZMNADoVb1les5V0LPc4CJEZiJWuPtvfj6zM7xIqxYTTR1YhTc2mOTiEAihbyzTKShob3eW8xfEcBWx2tmcut65WRqJ9p6vhHDyHWsmQLDcn9xfCtxWZPdKnQkZydniaMVjs/tB5UJttaVbIOac0shGRSxqEDgdINApgo+GxeH0pszE83aDwH4TSGEjwsh3AP4lwF8ez4YY/yVGOOHxxg/Nsb4sQB+AMDnfDDYuR/bBU3BtZUkjZpUimjIExVkk+qX3+iWhcUTwQbsGkNVi1HXB/P6SA3lY5qzgD8rKp3ohoRPqy9WK162nJFXDNUMps6Jv3ikepj4Qp3YvfnYbGIOJUHFIae9hBo6a5bE9SxAIefbhZnonGnWpCV8PbGQLYuBWUXBS7s/QfXOJ0Vja0NrfpzGKcHSanZuQhDG9E9ZgO9oZF5a38oCSR7ahprMZBXPqJU5BZjzYgenuiE6NKB9mWj6fTqkmiw4d24erekjkQkqxyL1dh2Z6Gsh36rEI5/jJRZTWUjgSmIxKXGlneyr4NaC3NCzRtJA4Wg7aVJxQzixVrlPXJIBOhCWruecG5J9XAa69tfnZVxJVB49xGHy/VyBnnSULF2uw6yC5Pz4IJBmqlTlKPo/zkFuE+Ux8mSTJVGYm0KaHR1lkG+2FmMcAHwxgO8G8BMAvjnG+H+HEL4ihPA5v9bjeayJfuCm2XL5gqSiet1o1+Cl6xUTor2gxRjxx775R/GP/IYP4D/4gt8l5E94lNeVNRjj5gNkGx7dyOs6ydv2hrhkdEVWU0kC1BI+sSkl1t74WqzeK8MnhxSzbDCThulHTtjTj9GM1GbGc7semxtwg6wpEsRe871tjdHCmWrENhRcfIlUmwEMXpOsvvNtPdiebTiNnUexy/re+jH0JNOcDcyN/EfD0cvPZs+4POLDDGKrxXhpvLdh3M+HY0UaFYu/L/1Ul21JeXyolVzDfH971xzD0l+jDvbJPXO87TXwUuelG0ZTRuk0an3P0zDh1qhnB3wOmoe9VTHUK2KqPFbpRHuyLi4H+QZmMTPKPdKuI7y7CbhRcO4HkLhiAYi8rXBeiVAEOexMc2sNXRxFR2b+9Wf7+VT2T53AYcRr5PqTQEOwxIIKMCsnthUw2ZQqCKSUynR6yxiUk13y2NTcJ2n/55nou1cJL8IwmomdaZrXUxGss2wmi528NJmYo7gGWbnNZgfp+ftbicPskpvUv0Iy2Hu41kDnc4uhc/qRJ8WO/Yh3kLn3Zm0xxu8E8J3V3/6Ece4//TLH8piJfuDWm0yDqyPXmvBl5LO1oK7kKppJUZN8VJmp5dq0IPyfP/NLm/9XkE7LWdQ10VvDmJF0+GrdOJHL4rSo2jHyfIdxwjRVcKYWG3FlZJXvbzeuQTlyyQC1iMOs2srs3PdDpE40g3OfFOmZsyZaoRmYxJRL0k18o2Yke4ay2eUXkZdfiCj8kSAoZL21iHKPU5Jn2WWal/4dUMJGhnCtY+a1hrcGPHec9k50N6SSgr18lC1b1JN3azkoa/a3cNjjurZa2V0rI9s00jYGoL3OWc5Lbv1ol1l0jvKVbhgF4Y+ut1XMv3mcqR9uSCqJK2aI5+AHW6cUy3C+j/o9iaxPZN7EfTwqF8deB2WtPaf8ZDTah6+BtU7zbg6qmmYHnFuhHShKoVGSsnGyCDu3VS++BghUOQZ3glak1D5Yl8eWrueZVAWXbzlCJZ8Ee/7DOAn99X0guFyhWU22qvkFbFLGfAyonn/xAUZEnMZowvmX+19Y9rK+X8XOfan9Mu+DF7JjW3wCpU3eWuMX226KV8mLPbbr26MT/cDNIzlTLli7zFJluO6NTkX3fx40q7x/rkHMBgSDWua+AANSuRhnds3Yviaa6LiyjTxuN8yn1e9LNTxxhX6JzJ+qUdn9jsoxztHbBeaZBgkgOVV1TVGGre4YqOfflmqeiRM8jRtnppWtXDfZ7b2nGGc4txFJ75QEVNuAW+q9hwkBTomR0nLKm8SoNSQB+xtVcLtUL9YIQCxBLwHndkDl6vEHrL8NYEEdZoTHYoMv69XW/1aEROkexXpUfTgqy2q9l2EzR1aOhNbvyOthy9FkMFbLiVfomKc7iaRsDLczBlqmyn4OCorXjSMxDh1ByJFLz3g0iD2SUP3ky/a0tVqz7rVdew+s87SddVm/IfYtq/pJFMEU5fCnOdnYu4o+PNByQM2/ayWuohnkKA1xKbEkeBOW3+BYowNilc1VaB37GxynCdNkXR+bgcJWJlkTi9nM5MD2u2zZTEriar8OrM9xlzip12EBybXKucpgaLnH1cH4ViY5VjaVrNkWmWITLTXFZB8I4jBV82xKXA2C3XpK9sn++fqUB1YntUInFvYeR1nwd9sN1R4RgBDWe/XV3KqfwjmSto/tsvb4dB+49SNny1M1DJ2SzBFR8lamObelBlM5HPP4jmoDIvIoHuNsVxNNoE9HB3xuhW5Zv4+zXNdZzRZnzKnKngPrRrreh2TUC4c7w2eXjXjeCPZOMK8n7AcO52JOeCbuMuFoIlIvmWMnbkge+xE3bJNTNT+OmlWAsXOP23qw6jHo8gtN2mIZt93CDMqzICaUfuIGjiKky/q+toHCdKJt46BvZKJPBiytZUAvz4dA7eoM6uow2IGHOiNXj6+d8a7WlDKLstS0k2+LrvXs2fvYnz3EYgxJ4dERHUS2xlV7LWV2dMZL1fe1iS73LSEEVAmGI+OtiDMdcG6Po63g2lmdwYKfW6VFm+OyZIUFQLhGuDLk60x0GeRSaJ1OoSRUOYaC4zqQUoDeP6/hqTmKgFpNLFrbDwrBwdapvL9a18eY4Np2iRwP5HlL7FSgeV/CNx83guTL/YWT3s32h81cr4P8lFRtrJNmdYCDJ1B6oaGuNNwf2/Xt0Yl+4LafFNtWZ5rV9WvLDpeDyGh3fEuSYW5Ii9E+1xg7szCtBcZD8LInFmsbY8M4w1aVYSPGq6A7G6eFyH3s/ja2fweTzCmbYvhUOs7daG+CMcYFDm71ne5tbFJXw7nVJsmZRfsxcgPPocN4x2qqh8nIoOX7CzZ9sUnVWa+ypzWLfxkUbWXt5UEj6/cpuDjNMBBHY2igNU4G9Lhc7+p3pDLR+XeXV52qNcw7ZpNYjHxfw8Sff8p884CpNa/Tc9FOmqq1BHQmVGaihSG5J3lr7weKqAsQZF4NgzUWuB5P/SmgEQLKaczBE/Zucz+AZkdP5zAniQcpkiJIkY2q6/1FUKAbogyAXAP3Zs8zl6SYEkMiCKuIK3UmWtW08uOSndv1DWg4PDuueHhqdu2yLTKKBtxasluLPYR/30EyzytyRrWHHRtQ9rJlpNmBqHOcVQ5TTT5WzgXkucnXZw6lB57cco6kx0z0y22PT/eBm3KSexOalNqx51FdH5FR22iXTvSSib6Zx8LreSjcU0TgchZsc39R5+ghlWHj7UTmjtV51vdojbvsx7pPk1hsrtW0ssm9JNwhGS1VE7RkKzkxioRzE4krtkmpbF0vgjHqG+0Ec/lp4Myk3SiyVg5HxqwXEwGI1UARUXxCisb69xgoFM5tPPOWAlxJYLb5u6Mez3JuW07MMveaASyme912BNiaUpe/tK5tPaMYS8K+88ZT98+/bT6+3IfKRPeT0JldnhGfp82xZjmwDOcmAbVW/XYNG07jsOfrkjmjTiXfY/sxIkY7eLKMR8jblGPmTpKSEdROFLU51HFh6PejdsLt9fk6+S5Zs0qk8IBS55nDuVmwC2CZaE9dvAcJwCHBmvjL3p9jbAcd89gAew/yEC8Cl7/fFc4txkeCIDQTPWkNbmmPX1Dvnhur9wfmJACz/SZ9vSLKfWzXtcen+8AtbSgMmiWIilQNqNQU5IYiQDJXy4I0Z6KHRPpjGTbM0FO1252RrQL2xoLHOAJ0VF8yLTqidnUddzm+5f9J9ipnuwEsBmTO0ljEYUrHuScSVF44th1Jd9ZEk6ykkojiGtcqUqu/UZ4BE078GKX8B480c+IpwFOvd5kBmY/f311m4Mn5LdAHm/P7tsPH5lw/NkjHStIzA+YNtI0qVb9M4dzNem7BWl29+zKTq413jbzprHkf8z20g8bKf3KrM527sRK5seWcRiZ64wALlv7Uh64vBISuuodsT+xdqoRk6We0kVrLOY669X6ccHuw4dopUHk5ZL9zyGJyQ91RQibWCpsdmc+xQZQT+YmnxP5pEQCKNdoD51aZaFaGAqQ90rq/YtfWe4CAO4sA2spTYzvxnvurci2WyWZcDP0oguiqFMKDwlBwa3b9KMZPro+IrmDsY7uuPT7dB269gB0rg4TBtceZZEpFLdWCpuqLFmKx2WixYXx2jZpifW2xVFvQHA/krbze3LAnBefWci/ruEtikHZNNGP73ZwvHD2lA82yrV4nWOkoK/ktFmnmBqljkyNBJ/WNnvqJky8NE9Vt78XxbuC67Ucyn1UWX8O5eaZaGZirxua+f0XSxOZ3m527/ZxYliRnYFrv1qqzZGuAtbZGRBM+zYjFVKkC1z/l71axc+cSl928Lp6V17mV7Nwi8Jsz0Qq26GGpVkRpytkD+D7hrVNWgT/VRz5PnXMctJyWyjT3DtnMS+Hr6zrAAxPMSWZZe5lpFhrYUuJK2A6aWEwEoWcn3wriur43D48NC7IMNm8I42cBSpuLJ16UE27LoHKegszJoti9tZN/GaKnN8p4luPCnteZaI+aDvMHtDypTcoXMUVd3vLYrmuPT/eBW68mLYGPxJlt0o7aOYyVB4CP5g3DY7TsatQ2sgzceLPuXy+Yu2gyIXkAPJp/9W9aGWSVE53JoDZ/qwICVO+6LwmWcq06Z4BWOtDd0DCmq7HYEg880u6tCWORZsXMymt+4kYjs27yG1XyHyoTLaCsLNK81vLzKLx1vWImVcRi/kx3G/rMxsbmdysTfawRBwvLMc/UUyIwBuc2HOzWs2wy/BaM0EDLCYguKKMy7pnxqqRLAB9UmEKxcxasxaI4NwV59DjrR2EMqoxfOqdN5hUqzg+fpBTP/PkYtUXAVRjYQHa0NRJABVnVb6YZ/JHArUUmd5wmDBMrp1ESQtfVPKvxyUymykRnhIRV7jRxFJmrJpqscx7kBCtP8BKnWU6yJfFVj08Fai8Ngiw2qVxH7fGzuTOItW1Q5ZMOvoD9Ol6za5N32wtiMZI0U/Xmj+1h2uPTfeCmnAZ23EMgAthsvgCHl2gSpq0BKh3hAoZUT3OV4SgzsrVOdW3oygzD7FQrORompwNoEoh63Gu/sXmOJbNT/z5GMASkhZ5HI21H8CQ2oRzpV5uUyoYyI0AZeHSTu7LerzNgxOX9KfEIifIvGcwLoXwqAKGcggXqKFAGZpSeZKJdpILC8Nucb0CPGZytH+yAouVQlWvIfk1qfyuMYZ69Q8WafLomEy2IxY4u+PM8vjPZ43PLfrXOdOqa6FM/4e6gIeHWOhRjTEHOKx1k5XTlflT2CfCxgGv0FA+WAAquHaSCgSYW4wzzANOoFk6yXANVpvm6TLRkd16Is5STbSNGrHpooPjejOeX9dEZ4gfQxHOK3dp2snVJD6Alpi4ldlNwbm8mnZUw8gSFQnnoYKZaK3gASyH1RpoEYOVuS5D9MRP9Utvj033gxuCrSfPQhsb1YkNZF+TroprmgpTrY29Xp1QvILaRqPRprb/V0DZV97Jen+SSrA1R1p846gNb484GbX2OlSmrn38niMNqCYvmceP6xdi2yKeUkbActxbqtsb1OjZNvEEDBNOEJ8IJVt+oJv66zEAZJg5VlOUFKgChoISTgtr7oI4tJ9s0TjLSpJFBy6GkYZwW+bbcEkt6QB1uS0GM8wMwZiaaGIVWZrt+ThFhRdQQHfJ9Fmb/25RxudOG39R8ezLRPKOqiBJba+JOYk/Cm30EZqqsAph/T0szXDDZA34HOfVzRSbaUcec7kVQLnFFP7ky0aJm+lyZvd3xC2uKVSZSvTe1Bqps2gL3vrCEbYHTCzUOCwnWjdxJk06oCEApdF12wq11QMkkSnJJZyZbBQut47qmff79BK4N8GCv4jzhASYl36aDndfYnOu+uW9jjBjGKDPRLAD62K5vj070AzdGib9Cmwyjd+ILvoJnUvjovNgCdmaqNtpdm29jLB5JqrImOj+NVpZ3A5u8vUEtap9OiUufLFPRj5EaR+2gQZtBfPu3sXlOa2NvaTamut38t/3vG6ZMLLY/lmrWbAPVy86tiMWsTEgmvbEM9czObTWViVZMuYoYicl75OPt7zQ9637av6/c26LzrAyAK+VZ7GcvskCTj3TnvnFYGyc2aqMseShRJq33zByWbrQDJLXxsof0tknM2nBu+zmy56CCJLQmup8Q0Jp3eS3ThDWAhnOzPjJTNc2WzjwcGi6sCLSEs7EE+xQs1uGw1EGBIrjBv+v1G6Jwb1HqsJYqtNeuLaO4Jv5JTrL1bKODN0KRD5Fgj5KNHLkT2MlMpCoHEiUtDjTPzSGkedYoWfBIDN3f2nBtBbfN+72pTiECMh4JR0BnkiUcWu0RMtPP+7/UplXkiCuayj7OMrkK6aY4XVr7YCjsNBWQ64gTvMiTWoSCO8Tj9vtWQfTH9jDt8ek+cBsccG0mNwAwo9sX9bt0wa0lW1SWz6oh9kT7myzXDbKxdB9htOTzSEQdyCyifEFkpG3lWDb9ju1MdDvDtx+jkmHqR9sR1DqVfBNdspnW9cLJ7kmkNB+3gmRXQiIAACAASURBVEZpfKrez95k8vWK/ZJKlgnCIybfIqGEwgBQmbPOcJSWjK+oie6lAWSvR9fU++ZvanO+UfvOECus9MWCwTKtYKtGlel1H3ub/b0TWTSeidYZHmXcs+vzOVQpIsvbKCdu0hI6HgSPB3purVPrXHJkmR3BW63b68lEi/2oWQ9Z9WV8k2Xrxygy0bZU2noPI/A+zXJdFzpJyiZRa5y7hM3MZKo5qPd89b08ZQiKcWy+m7jwrOj7AywTzb/7dX++LPika6JbmfT13CWQrGrSL3y/K7KIB0kspMBxGJvqFHkP7UXZjNKJVqUSzIbOGukK5WGuiQLFMAio/GN7mPb4dB+wxRgpm5/WPPTWD+2Px+hZMH1RwZVY7LJM9Bp91IYIUGarWploDSkr++TQXJ4dV/B1oJ2JPvWZoXPN4APtjSNlw/KiHpbzeV1wNKOR7U1uez+AwLmFxJWqiVYG3jBtjcRWnSrLoiQJClGXL4wUSVZkXL+UXwiJqUvlVRSx2Mqe/XIM1O27NUooKJzauK9RqtH6hpKOph3UYwGKfP/YzDQ24Ncbp3a9htVtZjROK8t6YuU3ITSyEKXxqQyk1m8vmbc1nHuPzNm+Z1OHu/qtwxgpJFA5CtMU50AhW3t5UMCj0OBl3tbn+Byf1nuP1TvyONrXZaLzGsmz/JaTrZyYFjFU/jwidE1zt6BhjDnuJB6zSz54OZGnZpUHZs7n3NghDQT6DbCfX/7uzTVS7BEq2KZrikW51yiCAMIW1DXtGu6unNw9kmz9LYNATHYCvciCwMM4YZhsNZ1BqMWoQKtS93jMRP/atMen+4BNZSYUiYIk4Rj3C1K9YAM6KuzdkDx1eZfAHMtzynY0MtEe6ZLc56UspOn+DomrVgZ93DJ40mxYv3cKO8HOzXSi3eyiAg63y3bGLTzWZOcW9Xge0hvLyc1s9RI9IOBSrBYzbbJWJNg3XzcbfEMLWJd3rMfLDV7J3yz1ZMpJN+vJOGICWNeyGs5Yw43LozU7d4YNnwvn7gcbBWCtTRzO3a5RrWHvYXONHVhrvn/ntfn55sBb7aN35Lcj6nmZ788zqpxUKDdN7ues3xaIFAAmSdM5e4ong6/WJA+fhwq4HsXalPvyONGSnftC3ohBOLGaGErBuRtOeBnAkuzc2slmPCgtpFHAtsxEBdbpt61qoiWvzHWZ6E5koteaaBGEvxAO3okggJorKhDdqumu12e1JiikWfnbQhkhgp2JjkvpBwn0kix8hB/Jpmw789lOfG49todpj070AzYpSSM1EcWCOfDMlYSPqqhutWDu6g7z+kLIhYD24lFXI7WyzmYm2knkoqL6iSTC3lCPjvq0lsRVvdgxKGyLLbIzmIvL45eyZ7cN2LVqpwnnLk7tcpbdqN0crLHNH4nHCW5dH+MKNVQGbWu+ld9oaxOKRf1jC+6Vxw6050sEyeShRobYGdsQGuuFmF+78RGo3O3BdsJXh6JdP8zGzgkM4+aezIliEmWModqqGWbOpZUZZOsyewcdy0jGSJE87Jkk2aA9zG9rPPJ5DzjWQ2emosULUDYPd0Yaq3aA7y2pPREgBnSQGFjXaqufnDVvG87bsXr2I1UepAK/gB2IzGsYK3tYsmEXwq3d5IiCh8VMLLj73x4v4biqpIGTxCkNbZWJFjXR6v7y+fvQgxaceXWCVSCV25TmcSNxFAviPHa9hR4ppVJbnCvL/u2pab8iANVVc2uHpCP3X1AEGyReGUDK9jx/91ZgcQ3C20HodPzRzXuZ7fHpPmDzMlFKyRpzQ1HC9k5okNhQ86bAyIPY8cXgud1nduqxqr+Vf3fVlymSCAExl5mDRrY8IwRuFqktO0LYimyfCEM1I5eI8GSiBZx74HDuvImV7KVl1qxv1HPXx61MaB5feX0rSyHZtYvnuc/obeHc5eFsMKugl5LfUNlGFtR6cssl19g3rzW6eXBmNXBIJroZ4OCyLDXR0VpSsD0/xkiNzJYzUzoyzSBVP7YDE7CzEtSJJmvgicDx+zFiivrbaJHeKJgfoLMU6R6EUCoWBrLMRHNHgcGWY4zSUE9jFXq8Thg2wPeJo3BKrADoBiHigJan83R5kCKQA/aByvoJMZkeFShZA/ftoIGSiGplojeoFLFGDSJbNjQcjRr1cg253lGgNTyZaEZcpb4BZisAZQKB71FmEH253jqu54NSPNldX6EjGfFoNzUczbDdO5RmO30/DTh32frRllEEQHWcWcANWBUHLiVVkyhAsd55FA0e2/Xt0Yl+wKYmhSYW4xuKiuqqqOUbnaivmR2qfDTV1KkNomXI8nGmsbbg3O1MtKfGOvVZ1x9VzNqE7TcfVwZlM4Pe1zrRRTAj1sfKyGhc7mvLGO3Jf1oGndR5dG3CjSz7wHUwe1IznPtX34E1HxTUMF0vMgXEyFJO8FJ+YRhJUp6jrw3IdH4ejQrqKE6CfkyZ7Pbvi4WB2WC0B3NcIp3DnuxUuW6cDLREN04z0qDVTwXlLy5lxks2muvAxDhf01rPmBYtzZSz+utG6c32Wtv49dSy+eDc+3rM8rkwJEXZBgc795PbQ5P9GNBoGQAzW3m0CYIce4CqL833Wc5pjNfjIHuIM9N5RYDIeDaSWGwOosqSGelEXRa4XzP3Yo1UTrzBYMx4XlL/aq3h8o+SoG8XxNi+p2M/mvJK6fpyf2l/T5YN5UEraYUHwWlCgnXpOA+kaXQfn5cqiDEMvKZd3Z+tz2uQnPEJcOLSfrSdeEksuUla7b+NRZ5NMLPbpXT8+kHYN4/tYdrj033A5lnw03EDPqokrkT/kqRiXjCftTRtgF29L4vypQzDFcRiSw3cYfc361zJzi2IXJRcUmdAgzfnjPsxtp4bYGW29o46IxZbIPYX1kTnb8KGBE04EHZLRXrWDzbUPB23oYbZsbFI01y6r453rhh/dSZajY+/G8bOfSmzZx7fPclkSwOT/P4jMY4Uc3cNP7XIaY4978ear8z5s4zmzhhzjJyAjQVp+kXibH9dR44BvFZR1QnmcQHXSVy5M9FXw7nz+9dlGQeh1+upL73GAVbfNlAbx3ZT+1HuS9dE24HKaYqpZl0RR0md5svQbavNYpespOPcpmGKJMFgx8/HZSDJEwBix8U3x+bPsW9IIIXtccBREy2CDKwmHLBrohUvgno+eg9UnCVCAky+H45kAux6biCN37p+nCZM0SYe2wfJ2/e37QufxKWtzCJqooWT/dgepj060Q/YZFR1ifpdVsOg2Pi8mn0m/LNyYpmDMkwJrngxsdjCKr1nnI2N7G061+gvZPi5Q67gCrmkcizlCDO5Rm5MG7JVw8uIxbSTrGqiuRM+CDhcN2hSGyVRpSSe7Cy8Dp4wOFWuB2TyFwAxACWcmxvTe6N++5zZswGygc2CPpEyj6t3c8waqC3maZLBkmRDlWFikdMsUELz+bWfD8sWqsBey+DohwkxBnMtUxq4rbmrMzR2hkixqgOrE84Zr4UBPHqJxSI1RKWjsTjrFXlhxapuBmxLZIRw1plBnu8DlHvgem6EdmryfdJYynMM5vny224yvFv119uyFlMdYc5mWbwIcq6KwL26XqGFFNqhH20JuTS+tDfagUIB51ZwbAEX9vCsWEHgpX9HgEmV4Nk8OQLOrcq9RCbaEyBjc06tQT4Nen5/O6nEv11FXKrUMxTKTyEydf++d6cksh5rol9ue3y6D9j2EL5K/DxvWDvK/fl4Myq89rFmU8SkJZlqtuDVrLqsXpFNYB9L6rpA5NG0srz5XGocLUzSqobUjiqOU8Q4cb1NYDX86/Ft/3+/8S61nBWRyVLzLDZBxVrLFloeyeeOGIdzR3QESjjFOGexFLGV5YxpncNTQ9M1FDB5QNeLlcZ9KAI4y3xUElcXchT0IzdQVD1dPynmUYeBqbLoLcfSWGfyc++nbb/HGap7fzhsfI0lE02ChlZtM2AQ9xlG83GwAh6R1mWywByraecZz0gzRDKDMKOAAJWJznDuBpQ4RrlfBMSU6ZzaiIbcq0JUHF1j3T/nFpEaDyyU664FLW+9l6I8xkFg5qnxzrXgqh5RBX4B7miwQE45VtMmkXBiMdcl+o4HJcYpcjSNDOI6AjxSplN8lyITzSQeleKHClLITPS8diliMdZ/CMA9yQTzEjhHpp+UNPQjt9mO2V5i5RC7daNSFmmOL0oUhkJJSCTVkolW5Wo8gKIkrurnV8/NR3bul9senegHbJ2ILK31QwoadVlU1xPVpFm9gnl6nCLVVV6zSCRTJbJswNY5ZDXRKkOc+lSZaAL9ERHZ9bz9Yl5rRx9rx650HKpFf3GSBaGOrHkmRlQKVFwGh+sGW4IK4PI3g4D6qQCAlZHLBhcrKQBWZ4+RpgGsXjzPVx50UsgPRixyKVQNSAYmq1fvHAamzKIbjqV1DJjLIjbfuJGJJg5LdkIo54JZE33eNUxGh31f3dBmPY6Icq0+DiMOB+CmlcUWML80Lp2JlhrqAmkBAN3UziJvHVyb1yCP4+qxCoM1j8OLJLJrMH0oKkYImMeSxqvHw8a8BlntNYSNV2XWlYSUyoSqQKcqybECNOv4NLqMB4G1jjAjvjz2IyfGHHigUmVilSOmvntLorLun8kksW9Z2VRUiq/o32qeTLTW8RbfrgiCK74YmzjMSQpH1DPo9fndkgBHGp8o1XjMRL/U9vh0H7D1/z97bxpzS3Zdh61zv/sN772eyJ7VE1ucZ7H5mpRESQxJTZQ4QEqcSDYVBRFAxIGC2FZgW3FiCEb+RA5iZ7CBCMmfwEGUxIkDJbGROJYChI4lixSpkBQlsklxHl6zm+zu1/3uvTWc/Kh7qk5Vnb3XvnXqfl93525A4uuvpnNrOGfvvdZeu6zhsX2pE5NS91JLWVelFs45Shdn6qSrosIlzSmPkGCphrA9V9iuOKyMxhR+Rx39Td6XZ9OYQ6Ih0cOMrbQspltzbWnopHdgp2octrkOkVpIi+g2AJEyzaTmeVXoNc1N+ywdzVJrnhUFaOZgMSo6Q4JDSYHYPqMiDgRdhPSkF/8e9UV0U8mtZwBZuC8en0rnFhIgMSuCJSB0zYOIUTFo3RFvk95RzYHs2pvZknRxS5TkmJWAXSuT0dR5h9fqBZZEkElTjS0q360jgrX3dOGSa00zPkKFJOU9gFwC5H1chpO6532qNhC+c5mVwhA/QKdQj4X4tsm23lhIDaZQ7zw8B6915mysmiSqAV5X2bWtzKtpnspuK9ski3B85RXxQ6j13AAvwcpV55beu24+ka8f1nO2vlp6tU+h03vfPX+xJrqs9D7axLdaUZ+KIf3a73dUHZsF4RpdX0eiOVLbzMNOBgHIN96Wy9CWtwSJZgmqiQmyg81jh7s7o/FaQdkp7wncKNQoJsLQHC9/lBZqUVwbNg2J5jV9YfKOf0lVC5Qdg0BL2C81IXu/XfAU+qwdiR4H+puhsJhAw02hyi3VkYhXMfVTrcWV5mQUpK5WU7cGdNGbDWlfYkeiiYNIHECZCk++V7IIUqSdLLKsh3a6Hi8KVmtd1K2sU8/OFgyE1ibJfssGsZv4mXQZ9f7+WiafBbbxtr5QT5oZsVKC2tCvO/Wep2sCo/68pN5fTkJWXJWeBJVND/AMFM4yTxf6NwjougRAJKKWRbs1IMQWJJohgwYEmTFEwj6APl6LOjqbI7sgm6hzCy2sLHRurWaZ1jyXHmfLIxHprKRSAd+pdzMkM338dvysj7AwXwSTOiT020tqJTNEXZrNE8X4/vdaSLKa6KJOlst4YwkcFe6s9CCbrmGljkSvTDXRZP2diESXhBk6FxItAS7at+9hbHGpJFAONo8d7u6MFujcqY/K+64nXq9dUTT3bRIiG72WJGRBYVlNqcYyLDhFFMTy1gu7IVWpfSSa7PhaNjq3ljWtFGSruYb8e/7e734J/97//Ml2LEMbBtaSGEfqGirVMWqBIQaCCVp8e7jfIslapp8EYutCpww3wl0CymBsn6IhcvrxVvEmVhOdvvd0kSXbm3vvcKSoj7OgQHMgyu0iORq674I8nc4tBQOEJq+IZpVVjaoeINECbV7rW6/pP2iMm3WZbimjiSpulLpMKSEGNA4kU2bV3n0p8LG2uKIt/wwBBLtON5frtfV6gKyzaZp9eEIJYKrZhnuSaPuVug7tCEBp2obEgeH+d0iz8A4qc5iPaud772kchBnEk06VILhhw0Cc49jaXdZeZUI1+go6e25J3j1TezZpe1JzozsW0MsUWIJJewd8Ow/L9z/0qk+toSHQH7e56/69Er6Zlq3EhMUKufsGwO/vpibADkOiNTq3UEYUjM2zLBGqJsk9KIuE+Red+C7xjwSfXutxfbD57HCHZ7QNWYBZT7yG2iRPmDTrx5C5xIQY18rGKtP21gtasKhPrvHkUQsodNiXOUeshttaT56a0H7nT67hG0+tmv0S4merURAtIdHj5EIq0OsnTvSJOIXyuQHaqC3yTJ17XdY4FqjmANo2S9I2QKFzMxSePDNryzeptU5KICgeCVtky7reooEyMyTpoPhu/Fz0hqAww3vXc5CJOvcAZRjWuTKaerq10zg7LiE2kgMZZ9nVmmwBpdZYIKm5sdDQjMG20T0iyt0aC0PUxjAhlPq7ofXSjs+hjRHgCvbNeWwosl4TzYMdDRFt9tFRNYDXqFpqmVcksGiCH0sihKujhzVZpHS2CusykglY0DD5PdXuRaOJoawPJPFPa6LLWk3gFFXdCBYKps1j4RuRfl/7DRFNDWn8rbicirQ2idaYXWidY4CoBWZvHu/rzEwNQsP1s4XFyPuh9XG2JJqT272nSfrAfOR0a8m/sCLRO9QsR7eio6MTYdZ4/JHv2DCleBnkwfLsEETPaClhMTfI+i5VdWwvtqoAoPbcBWRkr8sqknYDdRcMUiSatJmRtsX7xOeWlLmBQEPXX1UmYMLqRzR0rXeeYjzOsbCYgEQnUAMe6OkLtaXFBWthpdWkbUqdMaC1kKIOWvjt9Hi2SDEnR64X144vSNKrKDkCN1K1HS1y01GSopLvfbPdIrojPxvauiOF6g6emfdeVBnVUDYm9qVtSwfXOnouOeq6sJii3E1YGBoTx4REF7X6XbbPwdAmy0T5Fn5nUyajfwdsjgrX0QWEahWRAzj1tDkPp58CBiRauE4YHRP8asayAxIt9or16nZessLnWIYEq89eEfMEgIrURLOa4mQiMTI10WVM0p4c60wmialVVA37jbERtJpgFkRa1n82jwyfzziZOn187HsrKk7XZi3IRttdtw1QkuAkyW9tQSUmWSkSrQfZTJ17UzUsQSmeYEnUg81jF3aHnXNHzrmPO+f+14saw9y2KUsAuoiHXl8qZz2BsKDoE9awBqJHF2dZzShApCJlUlC6zb7Sfp0DJ1BS5m7GwicDqwopbUdkpOnFNgyspYV7nDnkrWpYL+WA0Ei1kbTPM0ECVoV8fFnVqGuv9DAlDhqhW3G6NEdRHHZD8WNryy+Ed6KsST0X65E5gUrYuz4JkgsqGiejDFrdp6a+3yF5KSR6Mdh38D1EbUy09iIausaQ6GRNdO0FnQqvPMNGVEi7f9L4A1I5habXnd9G1dbFHXnAeGOjJ5osyC2bwwCOmFkSqbZaZXbfbC2uurFKrXcsCL4BiW7XB5IcniosxkpmDGKdehKWIdH6e9FobmhIpr4+NaUljI67e7IQ2K4vjterc+EuO2NvdI0wBqXFFWOa7KbL4dFru0rEMVliqxRq2uXrRyMhSD8VFmNIdNm0ZmTCXzISrW8vFSo+sB2/pkdQ8s4ph/ZW+7eLTFP82wA+c4HXn91Y9pkGNFWtItEmJcQMak2MBg/Fe0bnUjL2FkRgVVa9xVFS5raMG85RZ6FtGzNRhXS4X+9vw/7ahVSXGTKf498ttlwhmeYNoeyGulzJAjtiyvEhkyoKg7VCeixBIGfyAV7zLC9CpCaatGIL5RfSQsWUZRm6tlEWOda+CzAk5UqLgyLTQKnqeGJ7SghPqvvXKNtdf9kUnVuub5aQ6K4sInWt9G/dEKRAo+PzUgNZ9d5K57bUIbNAfKEoJ4d9mrFMCzTisajt2CwJJcISYkmrZiw2OjdLVjPn1CJUydobAR27TZxDCZrGdFLCuy8h/DQRSHrVa2KezfFsfapFJLjZLgtjsnd3Rb4RhuLTBDdhOgFWurWOBB8fKS2qGFuK1Pdb6OSMSaE/f8MaqiSAvJfnZ+YfsCCWKtdnJrDKSvd/VkWFM9aeVGWqHZDo87ALucPOufsB/DSA//Iirr8v25CsLmvHQLO6hho3pr7NJqx4X2B3BKIVwzCopKYc7fS+44l6mP/nC561PRhBMhK0880ARZeR6DCpjhF4WfxKRyvWpIXVupRbUAFNsKL2uSxrioLQFlVkkRGD8Jq1/9Cd0JYqT47X6FzLhVJ+QZBo3iNT/p5D+y6WxdefrR5Eaw6STvWWE2hdsipOFKVbHGn1/oXyvWo0fgmJ1oIaSSGWOeFa4Mf6u+pItKEu19h/VkXBCl20CNCfNcAZS0BznxYOOCJ9lfV+vIY1hQQcQPN7dOXt1DN3o30swfr4PLvu46kmRhtkS444mSO17grNGEkQTOjcrIVXIfRaj88vvRd17YUOBI2x+Z31L2frUyeYxzQ99CCWfTssSM3qv15aWkwROneGMFhpEr+cluRgNcVtEMvo3GTu0xKlWpKy8R+cngAhLBA1AVXr397B5rGLusN/G8BfRtci+EVhm6oSs4IOnlJnqRKlKVsfb++HmpTOHSlPrwgdcJhlkxUfE3S3LQ0nbr2wKqvxftG14ol6dHejdgCS2nCRcO7Tv2d4f4b3MKHOXfX3SSHRDj4ZEDPKUQhKtGw7E+aQFnmAo5UbBYnWe1T6KBjQkeDu+vF99FSIj9U0twgY6bGtll+oyrFELd9Q07xTyzXff8/apJwff2MVcTABqPV4EtU7bu2STBQN2oo4+LZNy1CATXNyNTq3LmqYpkduFIdnU6fbTTFhGK1ej6pzC++Gg+9UW2lwY0Ci2fvJhLiUQM/1BLSkdcu3aJgarJMguXe/Eu972EenfHusjHRu+d57lcERzIJEa4nbrrZar4tcb0vIxD7RpL1YUXE6sRbkF8n1J6L7kppWVhOrBZHt2rhjYtw5398+oQNJvF1rLwnE9767Lw5xi6npdG6meTJOgox9FRMSLXxzk4W/tlYqTIZOLNbyfMbj677ltC/a0rmFch2revfJMn1/WJKSJbmleaadGyqZzRTGdxAW27+dexDtnHsfgGve+4+R/T7snPuoc+6j5XaheL5bQTJDJaFzawI3gE7/BDh9lE1oMZ2bI9FM3Ic4ZwNhnRiJHqHMRZXsdTg8nzQeoMnaeTgR0bYIygDdxB2OCwqew300JLqbtB0N9Bjauyn1FlRrQwmBqi6q1ERvmPBWS0WcimTrSLC1pk2iQrKaRKZuzZRptXY6ZT1uBdU/liOJ2vhMdahK4KLR6CTn0vu0KnVDPT4a0dJUWrjiwEjfuk8k54bnk5Do08E7GNrDSWNojlMc/FLv0bke0Ln91tHysPX3tDjX2tibMaSSKEMFdT35mH4WKeSWIcTjexmvA0kBIQAert3REtwmWU2Rk7uuLAG/vcVVdu9rRjkNJSdKIKm12SsqrwrUsd/aHJ+DpHo18aEh3a1yOU2S6nOsmAgjiZB2jhXXF7mEJBhVi2d9msn6zkT/tG+zqznWkxzS8wuigyyRzMqxdhH2Sm2XQAbWB7r1Ga3vhxvPe6qyPSmFWAk9vtvzF1wT5YBE798u4g6/A8AHnHNfBPCbAN7tnPt7w52897/hvb/qvb+6XC7Pe4yTrEHtWMCi1/doTjlbkHh9DZ8Q230JEq3RBZmD1+4T3atU/+XeuK1Oy0QlTotD432TeU+dt3euhOJlc40xykEDydJjsXCicFhzr/Vnyim/OtIsB/A8CAWgtAjRkGxwJNW4iDJRtqXUUo5k+RlVUUtaWfqzAvr7qGWyTb15FQdNm0vWRQXn0o5967gcL3p/U+unU3RuExK9GP1dqpHTEDmpFQhTr9aSmhpKHbZPVX0HLDRNW/uq3H7HVtqy/Ht8u88UvY9hOz/mNDJ0ZlPq4p4Av/dAh5La+kTzNZkFcrI6N1PX9urvlfqut8fTmmgWhDO6uDzHshaJLEjmdGBWyrBdvxRRr+b6us+mzhOGNoi6sJulBHD6t63VPFvUyctKbnHV+aDyOgTwIFsWngsJKomJxuniWts99myb9l76s1WZcIZ4giUVD5Zv5x5Ee+9/1Xt/v/f+ZQB+DsBve+8/dN7j2Idx4TBCnSVCRQVp90CRZtWpr9u+eUC3QMnOk67Qy1VS+yjdqhgHo/H5LEgGoNV+2bLSjNo0tKGquPdeVLxMXYNN9JuqUnujropKpXNrwmB+S1mSrm3tsyzXNFvpcHJNtY6yEwX5Qg8kw3sl0q1MPUz1TLLsYJAEAGnfFZRnWXAjbQ/CZVOCwDDPpO5bsk+0QBvfbJ2AFNMgsBykfusppJYh205weEohY29xomUUY3p/VovzowmLeXiKwgHGYJAmoniwLq074UnYelob1gDTumO4zgwq4J1oGE90WEqsxCCa9IlmvbNL5lMQ4amCJB02lV5z29REp4+val1du0WiGZJJmEomuq5yfpFtQoL4sI/+jeoCgpuSCYNytDMHWNDGz+ZPD6+isZ2/NJVuX+FIYfSw7h8l0dHZENYKYxk0v52JLSrzqoEpxzoaHCzfDnd4RtsQkQ6LCIfaJ7ryOr2DiIBojlccIHpEzvAENWubEFi/lmqIRH/z6Rv42JefbLdZ6OHSeID49+hiSWzB0/7mfaQYmbwvY2SW9ZpkiRlG19a2t0gzoTtPbb/VqnOTRVDuE82FsQALEi2LtmkOIi2/qFlSSz4/E49iVMD22UlUdwNVXhMuG31zLt42XtyDI7FOJKtWdtQ4mAAAIABJREFUZZWc19aFhsburv6vfcOhH3rK4RkrxG5/izIHeu9JvabOUhi3juvG1WhjcIRYPb+hfZIlGGQK/LZe0yQQUFgH3XX0YDAkgfl9I2rCCkOgO4fh+Rj7TTf76OMBtDnYb1scaY68XhIyle7bHG9gz2lJAg3JVGtWgXWlr0/W9YEFafL6QVgAhufL0EqKJBNhUFMfZwqUpMdX1TVKLclBkhBVrSPVzKejz5cpy7Oa522SVwqS2VzB5tfC0t2DJFDYvMoYTQfLtwu9w977/8t7/76LHMOcpokwNduJyFNFqDmVUQQiYfVWpIFNWPG5AJ3GJ2XhViPKXGKfIhLz8eMA9a/9g0/h7/7O56m4xHC8U1vO2LKuY8p5GwRvA8GVgsx0jm23jVKmt/WRkq1Ipnpd1CLdrK2nW0wTpWnrTKk6t7xIa5lchkTzRbZBLDUqPFtks+jcinCXVflTRtl1KjxrK0RRVgWB0hyvoFQ/VN5Po8OykxHE+qSezxrNW9omlh1I6twK3batp1Pq5WiCRWEFmcphLH2i1cDUQkuWKffNOHRHO1xHVwC2Il6G+mImlCaU2gTbEOSu6R1uqL3eYT3RHW2dchrQKE3hl9JxmSo6mSNz0LSmlEJCCnkCodk+jc3DNDHa+YQlaWkQmIFEk/dxU/AkiM5G4h0GpO1lLc/R4dqAgvQSJJj5dExYlLFbWPJOK3VrxqeL8tH2cJUXAYbm/KTencQbBenhfbB57JCmmNHWROm46euqbC/lBQVoFg2W2WZZV42i2b8Wd560mklWW9xO7ts5YBjEx9dpxs0RBm0/rQa1p5x9tOgpLYaFHOgWldR1A5qrI/TDoLO7riws5tW6mGaiTW+vtsIe0vEbmslnSDSviZYotMD2fWXMjaMFkgrv4I7zqtAdPOagMCScCXeMk1rN73DOU1ET2qe9fe90B4hR8TUHaFKA3X5nEZ27rMaO+la0TkMBgPTvl+q1taSKSPUN4jeD3+O2rfqa35JIADCqnzI/1rX+bhVkngf4HGujkurqr0CXxFtIQZqBtsxQZFtPa/J7DUyiIBbHkTn5HJbWc8159O/be74P0K3DcjKMtTjSk6xNEKslKnmiUT+/PFd4H0op9EQao1NrfXaB6Ylz9l525UIECSctrHLYIpuqUnUvGrRUTsBofZYZ8BCS5DQJIflktc5Es7aw0pFoHQnWzm9BirXtjfJ5Yn7dvi6N/oKegMrxT6QypYPNa4c7PKMVlS4sxnrilbVXs8JFRVBHhT4aJnwm0hCfS6sn0ZxsiUL0sS8/if/0n3wuSXlOUaWbcXFHA+BBR0Gy/lKdZ0qxfHgc0NEdu+xoCg3rb/MejRrs0WKkXBxfX1skNVRkTRIhLRItOiEsENOVYYODpaIkqoNAekgaAk2mnq3W65Gao8Ig+sLKC6iwGCmnkB24kOBgDqac5RfnEiXQSCVWmudsYax01rAQ4rZYAwEpRQgsiUQrQafkLGlU5YIg0VoPcaoKb6Rz67WOthZXLDnJ1P9Xpf4ehbFcOuEoMkPWVdRHCYjC9FN5zmriQY0V8U7X7QPdm8yCACBOdEpzOBdf1O4bE7FjaJsWaHiEsob08WWlJ6I6JFoPonL7RMslXtZyoWlBYNiHAyP6+6gn2fUODNr4mO9lZlMRXRAG7LASPLUkSfpOfeP/aN0/GBPN0t5LS4CwmmheesKEjHW698HmsReG7PULxDZljeNTRudmWUkBVayBspInpJDZZlk76aMeCntZlMB3RbH+7u98vjk2oVw6DOKH41b73TlHF6wNWbClexcrlqfEz9ps9JFDWXl1YQpqjnG7kXWhL4KbssJNtOZZD5REB4woyLJM/5o5eJXX1aHbRSKdQCnI98Lat1AHhInmGOjclzT0bEQdjepercJiifHHKBZDAUQWQDIY2O7r5W84jI0mB3rK+zVeMvh+PUhgW3oleSMJ98lslKFD4wYtpdSkV+I+hASUlJTcKPeIOa8locj6qPfy2Pr13GwOZ4h3cDQlM9V9lhVuvXQsn2M0d6fLfyxItEXpmtXo6uUxHPEG9LU4Ppdz8vwKbIWjFsocR5DoFWkPWdRyGx3JpwhJCe/1EjEWJDPhSTpHhpIVpo4tJQqJz9DbXiS2K+trPEfnlA+wMgZNM6UOz4f0sW7mPpllN9K/QDNvMjp2m4SUkOht59oTgQnB6fgWNlVi2/ZyRa37Xtq9C+dnicw7bz7Vz3+qJxf18k7ZPwmisQd17v3bIU0xo21KjxOtb1to2ZNozB6cObmnnVR/MmzxIQXJzYzFkK/4XHxyl8+l0vdaWrMBiR4udIl7B+/pghWQaI2eLgkSaWNclzUcumBRqnPzPi0iMpooB7+v32Zp/Nt7teWJbcBwkevO0SLJg3c2LJKp5xQPc6NsB9JIbhu8eI6E5YjehECDqdnLSLTugABbFGf4+7b/W5MemYXgYDrXjR3QsvDN9ywLPunvex+JHrxzBv2AXZJVcd1+PLtp5ymV0pfUc3XwfSQ6+o66b298vqreUhqVdl2p40ICSkai5Xe300aQRd+YKiuQX29rCfQ2ZUX70LOx3NjolFSbOJme8GIOdbMPQwYHJQaDudh7PUkaLJQCqIlfbJkYClMH2NLpJUfZc7YMmwNLNQiuEyKZ3T2pvRfVsx28GCSHnyvNgcE6UbvERu+79WdH4MBte4v3g8jE8YbzHy8WYqmDhQ2iJW28D230dOCFCZ8N58Nu/Do7cUPedY0OHZKkzXaBtbR9P85S/gW8uIY5NyyH0NiFhgREdG9aT2tbaiAx4doEE/MvCIuDUf01Jl4xTKBEvyN02jnQufdvhzs8o22qSn/pFXpq99Lr1BeOLOiOG+v7HExDm3zrbEyrRUnRGaUWV4ymHV9zeM7YAu1s6LA455oJUUAlY4Q8Tefu16amHMKYvje8p5TOVclIUJjIGR1bWoRaJ4W8M8PztzU9Qk1UCMILpR4b0N+ToBCvte9i6NSqqMSa67Y+kgTxUjlDUGIXhbHIAh8cSJkZYqsHE5XXS/3ZarRUS62gnKzqC9l5yAGQFmhoLIDUc/fQgySJgq4pyGpOWkHukSaopKvae9G5Ct+dltwKxgNTnmQCgM8/fl1EQQFbsL4ijA/KNiLMiOYadjVsllhIbQ/33nKdcC0NAW724eroKcpmvIRZujewmmsx8c4S05VeDsQ6YjCkWaqJbr8DIjxmEZ4E0gk0QBcJDefX6slZkBeQfE6n1n0pibHTlvARcUqmTq75VKntw3IFsSY6nH+kRzE8XmciqHRu1R/X/YtUqUL87a0Jy4PpVtjUufVkqgZgAIcg+jzscIdntEZYTEYXaqXvLBNpaD8KhiywllRGJJrS2rT6axKcpCbnVIAa/90iLKZRe1lWb+RkDxbqeNy944r+Qq5RQEUkWqMUJcbdm8iV9jEt3UwSFguUX5LJlu4bbXFloXMPF9BoKA3darqDmLq38fkt7V/GCYQOSS9rGSXgQneZoilG9W1RtGUUZLnxNqIfkLKgqB4nqxoBlXCu7u+aaJEmmjK+/pDCnKqJTj+rFjlXEmipbUWtJ0HS7Q6bcfad+/Hzk+pIPQZzDBENG2laOIf+/eetA2+9tMRzm/Tc3JxDRxvDWJjAWeocwSwOIXP4LdcBDLRxkjwJWJBW7x+fizMBiEIvKTmhgcSovVvUro62jRSQwnDuTGGqUBMtBbm8hzmnAy+I8CWgsddk0a7e8WSOZ+VUehJE9jlZgitXuKsgaCdLOLVMBKVsB7C0WVXAHNIyln9bms+Y+sZdb3sW066Q1bfr2qOsPZbh3g3AIcbyONh8drjDM1pRViJyxZSQeRCtfxQeXs8qt8Jietax+2/WczrtANSet/5JKX/LdG4jEm2oH9GyjmIbnii4D5O2C1AromBle21NrTaJRBf6QtwIi0l0vuaZs5owmrghmXhZPVNHAorKi98DoLMdAEsfRt5GiAmLsUyxdO+Cg8c0BriozkQHhzh4AUWSEigaysJqFTUGQSrxICV6tEx7qQQH0vU1VFlCotcK7V0LmBgSrVH1GIugqGQGRXw8a21HkVBCkQ773P+SS8p2XYAyjCUnMGW00ngfnc5tQfZYX15eVw1wFKq7FqlJrzyl009l+4Tzc+EpiVGxRYppRww2B+rHc2GxiUHW9v5LdPrVdt6ShKcYk0lK7A+RVi1IB+Q1xofuG6SHO2tBxYJs6fiS+Kz0uybfkoWuzY5X331Gl2YtrggS3egRaNfXhb+0crc1Wd8Zy+Ng89khiJ7RNHoFp8boIg2MHsocDTbhDFtM0QBGoKJZ6H0pqumqqNrFJi7fYQh6fF1dRdSTgCrtPBU9JHoclK4G2WaGRA//zhbiTVlhqZQA1F7u0yzRuYN1NWe6EyIj2RUWCyf3oa71PoYsUaNlir33pjY/Ods10RxeN8yE7mwOBFMulZ+dnkDRW7FNVz1tWiINEkXCfdbOowriCJoLmur1SkCi160jmA6wnUt/Pwxl25QGQR9pLSi9GJgA0buloiT6fOh9Mw9rTh7Q3Df5GzQG60axLra2WVSzVWSQocjec9ScJKPja/EgmgfaVIy0qnC80BOFkiNe1w2iNdVnsLZAkt6PkKyT+7eT6zMmFWGn0dZr5N1uEuAkCLII3VEkV7//jM4ul/ARYTXW8YQEah2dPO/4qfdnXVa6pkktAxRAWvOkf35eE82QcOm3V1ukmaH0cpJfZlgdbF473OEZbaNREAsd1WOoIHParE43y+oFk5zOeH9NzdqipDpEolMLlkWcI1yXo5p6QJcWFouFuMaBS/dcA1VTzv6m7mmzEOtIgyRWFwIACQVi6twbUtPGxKk0Khlgy7TSZ5bY3tbEkUWs6ROt0+20RaxSyy9YTTOpByR9htOicNF2ppxOUQ7ZwWHBhkrDLsdCSFJ7osZJFVCquhaTRxINf8hwcT3RxTQqoCGY4f1MIVWsPYuGRLPvshTEmtoxGxKLLABgSZx4v+SaFWqEjcE6Q39TY+lqX/V5CLApb3ftB9PXafYxouaZSd32WobzaCh/UTC0TGeNAJq6MqHZk8RF945NY+NsiE+0IUlgVq7D1OnZM2QJcK1NYDgeyEBaaQtKq09IEsEiEs07nmjbOzbPLh0k+ttZC00myqb2YWblZAoSXWz9B2l9q71HWdfib28BEPLs2behzZkHm8cOd3gmK6p663QLHzSZ8O010dMmLE4P7ZDoINLDaW8JtNWoCgukeslqDr2Bzs2ouRMCurhWO9yjuIaqo80uevtLLXOGY2SU441C1+sWUX0RksWnCFppqDlTM72kx7WmXtmIrvD2LUyhmDkxlC6l9JAENKoccUBr/b0OgnFajT+gIc2Ng5Oq9wsIIpBeZDclp5prdXJDp18qpdAy9U09vfxea4JoEhKdOiZVWtK7Dqvni641FHwSgwvKSjJSitl8ZqpD5vMqpwvrjigdK0XmLUi0PdBm9dnqfTMKi7FgPFyLIdqF4lMAlppo7Ruz0Yl5kCt0FGF0bSM7T3oemy1TRG7jx5lK0vztwYGEVVHTsgtLyzVWE81adFFhNRKIsT7ZYgmgsc9z6vk1wqNknaF0fIOwKGG6qQkqAhKslG+LtcJr24MRUTjeo1wupdOuf7D57BBEz2TW+hwm8iRTdzyckxEKSsFUJqS4pU67P6GiSFRarSY4WOe4dmORsnpmYbGk09KhyBrVHpAn3D6de/wMh8FIm/1tx9JHsoeTWuNsESSaZRtJXa34zpGas5AN1er8tSC3UaPXa4poexNyb5hzzlD+ZoGX239Iv51T0VJJr+46lFkioCgdCi87UE0SrMTRwuFosehdd6hsm0aiS31syn1v1Lm7MQVEPxmkKs+/VBIokvOk0Uul71s7RrpOUJ4HZAffgkQnz+25amwXMKav7bbtYZggERDPq4nWgdtrMfXn7h0anyMwLnRapE5PX5OkDtAEVABB50eJrdR3n1e/3T9PPN7EtQgS7eBRKOJCQDMHN/d2fH7WZm+jBDnN+Gx04hNFqR9IzzEOXYIltbZ78CB9XTalIy4hzhe6Auiq8Wn2WSuspiQ5QneHKSVi8XbAop49YE4MtrM+zanfyLoZwEd9shV1bQePSye70bG77h7beUxJRGuJZJak4HRqDgKwPtBygmPwbIatSxkLhLQf69hM0vpjY3AeLN8Od3gmYzWIgYI37snbTBBB6XA4Kbc9FRnFUsv6xT31JMrcMIhWqJZhfw3ZsdD3YuXkXCR6IzSWbydsglSz3xOPJZ64VtuJPiy8XfY3TV8dCYspdTtNKzHZiU0h48C4FY5Ys0yR5qZH7JFElyrkHqaALswFsExu+F7092hKuxqg6x08XuD7Ss8Se8GKRKcdGN8irZpojUUkadT+JtrORFGA/iIefyuAznqRVan719Vow6ITtGUhaAhZMvhX0HW5/ESvo5bmSw1hDeKK4vgVVlJV+6Y3r6opoQs6ARyJXhmR6E2hv0c0WDfQxq21t7aaaAviLSc+vLexqDiCr7Ngwrks65qGlm1KGU2T6LzhaZZEXZk9l4IEuRvCQpCYAV0faZYklsp9ujlcRyr5di3IpgnwQu6c0Ryf/gat7dS0RGr/eGGNsPbJJkn8SxPp4kydm9Hx9TasBpaM0oMdCDXTu/uMzdhYd46xDwwkEiTEXx91D2nPr7M4DjafHYLomSwVGML7NkheV3KQ7eDFINltM8yUzi1ljaOsKpCaEJvzr7bUqODUszZVErJjQY7HNW5eDCY3hY6KxdeldO4JCrJxciEIDcVB61Bdu6NACchWosWVlGkut+ISck2z7lwy4ZGCbF+RREpRVdtFJo1iFYqyOBCQSJ0qNql9S5tEkBMUQZRNYi90bAk3yiI7+K41EqWixed37fFlpbc26zsQ4/sbHCSRJUAQRB2BHTNFgtW+KVtJHecwVovfJO8DUKNByYbzROxApxCuoEgvIdHHR+PEROg5rv7W5TKxTaGtJ2ra/XbwVZsQ3XGuhrGdU6Ejt93YZcR1Q1DGcNyqkPvQNtdhugYyzbpr2WULtrT5naGq8nWihI8BZd6FGTWeOwdaAQodNdybTcnbBEotENNz0JgNw9WVpbIEnZIakEoWRElrjGV8EpUbaObIE+G3h+0SE6ndvlyM5v/u+hyJtiVk2P3XkeYRMDMItEwdIKLfGCchNHXy8L2IQfT2/nU1ywM01kBn15gyUqJqgY4poN3/UhMuhd5erqzSNc/DJLzcvk33H1Zkjtfbl/pDn+hztMMdnsl4/akN2ZIca1o/YnREqEhD+G+DwEpSjIfU0QBRLdRxR8WKg8k6mtBXZNzxdbmwmO6MpIXFOjS3cRjHjlA8Ua6Uli8ph3Ot1E52mWC9JlpyJLqxy3Qo56DQpZhyOKtDJy2uFAc8ON9qexeWqS7lOnirqIuoYWB2gDTE1hoEpc+/XMgsAU1VGWCorUzj0/oqA+N3Qkr0FOT+SEkvjUYvfeNaULsp5WBS6i0NNMJn0jlFByYS4wLS79ZO9b8ZKFt4LqxmztaHmNd9stIgQBMwNKDMhppojnjz3qoMzY7HY0Oi9X2akpn0/W8oz5p2AOvTy+Ywhqbp7yFDwxgwQEvcSlKvT9kY05lMgAXJNvYcn1hX25VrsTVuWpDO1iBLRxiGBPPjWQJPeLfqWkiS968vJajqLSNIY+kBFo2iaQkiWs+uJLqBLsnLkn0Hy7fDHZ7JOmQovNTDYCtkDacFybylDss6So5IR0OOj9WyeFXlRQVZS9A7vheuFwSHewEoTssgMcmFxaYpRcdobnON/jg2g8BaC+ZTk36TLdeFwRhdb6owWKj5SdWUteNVUaiQyZWRbjETW8vvkGXszT6yo+p98+xYX3R5kWItJHbI8iesES3RHQTWB5glKBgSnUJtw7mBdCJMCn5jVDFO+kjnsiT90kitHOhIolCtiEsCgdSCPCYs5uGSQduz64Y9c2NTjbYBUQIjOd+EwGOMjA+P7+5BWrRNb9Wk338P15YBqWwSkdK4LVMaCeiMz7XaJmRFld1KCubGKHI64OyjQ9Kc0CZUkvetY2g1++QldQG5Jrc3JoXOXW7p52z+Z0jw9D7L4buSfRoPJysYV/rzKLa/XXsv1JZmpI8va5FIW2BZxFczxP1W5PkxYVFz21NF10NNkG1LbvSSJAVM2bLsNHVtKp5I7o02/rKWE4S0ZpmIsrHEH1PPtiQ4AOXZ17ZynYPl2yGInsmYWp4diSZZ3YlZ43VZYSkgpEAz6YxqlAlNWFOzZq0lhvvEC1JPETshLpGac5nSqUbN8VDo6RFtJpWZHQYrK2XhSC3amqPOECfWBqkNwhWFaY0quCoqve5SQ0m8TudmCFWgCvL2LTqKwZTLxSC7dVCE77HWM72sv3nTPkMPTlivdY0OHr4bcbviIGlZcO6Y9WnkUrKCPX+pplgTK5LehxY1VpTIh9u6DgXyfC05gCFof+iOK8ljVwpquiHoHmBDoi1MBoAgNbW+ZgHc0WbrGhDmReVdJw4rwANxYBf0iCPRrHWMpd7ZgkQXpezos0QjQ7OoOCJLBLbvqhQEa0kJ0D7QTB15zer1aRDLkWieeCcJJvLtANPXEI3RAljo0myN5Ej02QmZGzRhL9ICc0zHH4xPSYKwJDmgM+U2Kl3agvLbvj1W8iNv159dSVpovtDNOfeTzrk/cc495pz7q4ntf8k590fOuf/XOfdPnHMP7WssL847fAHGKGkWVFA7nqnB0qxxwRaEyJHxclDZ7KsE0YLD4+txv+VRi6vtfw/FvCzZNIleHkxTvK28VB8bB6KL5D0c1kVpgd1QSKusISoXA1HNk1SzTJzpTVlHCs2p7XJNELC9p9oiqlCGi3qLkrCkDwuCJ7bIoplcI12KZ/nZ9WWURUNRGBWQ0rUVKjuwVfUVk2RyraImFNXUkfnedSUBqw1x4KVSAVWBW3gfWjQ0+XvkpJ9G1yxrL84nBRFs2hRyf9O2vZiGsIV7oKJwDEXirQg3JJEE6GwQIG55yL7jvICfBeLxeRhtnKGLGvoGdMrSpo4SFImW5+ikDktiu9iLnTry7BvVlddZ4p9+J0Q9mSrHMzo1VbDnfaYlZfIwPkt9PUuis0QvC5K1muNTYR4Kx6tdVgzPhyXYtJp2SqdX13/93tXeb1vSCvcmsDQE/8OKRHNNDeI/kB7gsjo372jwQjXn3BGAvwPgvQBeB+DnnXOvG+z2cQBXvfdvAvD3Afz6vsZzCKJnsvalJugtE3mSkL9WyXJi5o3L9Xc9D2XRpbCvokAtSOvHFO1VMUbFN1GA2RPzIgtRfE5KPZKcASXju6n6wf8oGCj7C4k2sYdFuVNQDi0e9GznVLodqwPfVJWKhjbCK/oiKmdybVRzMSlEWkzVPiiykyy8cH5GldsoQoCAQeiPojgEZWEoSivqJmwv2HYl2WNCouXnFr9zUgDUKfOOr1HVTWJLFd5K6jGk5wot69+9J6kEmiIsppUqGASRxORRpSd32Ji7axAqKkG5mrEYgmgiPsh6jgN87pZUpvvnsAWtGhtLeye7sfBa5w2ZlwHOdOjGVCnssdCdYapPMJM6t8LWOHI6UqrReQtSj98k7LTt0+nA4Xi1c0ZJ+kQL6t7xdmD6/W/XSClJzijDTPeGfJclTQTzIHxqC0y2nc1vgS0kaa5oXRsACxLNvw39/Pocz9qXtt0jXpw10W8D8Jj3/gve+w2A3wTwwXgH7/3veO+f2/7n7wK4f1+DeVHe4YuwVi1PRP3S7YiCdbVj8oK3cLIDoPUeDdv1llUdMtW179EzoKkJUgpIw4TeXEsS2Fr0fkvzb055a88p1cciLIh6/UtSWKw3ljQdu0eDFyb2FIJmzUYyup6ERGhoI2BRfuVOBqcaTqVDWY8nNcmSg0nah6wKvV7QIoqibS8YVdAgSqOKrlEkWr6+9mwkx7tLDPWvS+ncqbprhUasUVClxEORrInuj+8kfk8idVtRWExBCHkP8HHtdtfKUK8zBTilvrkGEeUhTACg+x36e8bFBwGORFsUvlVmBqm/bMeagQw2YzG0pTI8n9qDCh/VtZ5sa5lKjG0zFYkmYm2s7rNQvpFmux4k8xaJVVYLRCaMqc2RRdkIT7FElk1YTC+r0YJgQGdLLZzSwaHidHdGh2YlRWx+UWvaSSJ5o2xn/kWbIFRYeoDeuQTgIIDMwghzBPGfhHihbYE1cQ16gdt9AL4S/fdXt3+T7JcA/KN9DeZFeYcvwtZl085BdPpZjWXVKCVrHwVDrgA9INNbVnWOjDUDmlbITQSGrvuogXSGdlVUuHRy1I6lP25OSdFEWkLWkdWjSxTReCwnS9eryd4kgoa0Iz8O1DejVl99Y+8MW2RZAqKoBoHWoJXHmgjfaOJVLEtuQdGB6aI5PJNLkGgi3NG2Z1ESTWOV9qi9TE2EvzLr9ZrWRPJ88SfffEZWdd++N0m6sUIDDjTWeOEPSSibsFi/vVi6vEKuRZco8KWCyGyqpiWXJLCmBSDs3RDfTYVBwepUgS7hKQkCNvvo8/1GSYS2+xA0BeBo1oYEps55KrDF+tkCPBBvx2rov81bO8rbndNLHoIVShlBsFDLz+ZY6ugTOrWGhmnU9UL5dprteW0nR+vTwHJ7mK+TQXIsaiqPv/WB4uuP1k/W55gnklUdG0rX5uKUrK/6CHiJfqOWSGyPV+cXnY0lPb/Q9lUVFuv5w+P2WCxB2LFwJgbpDIluk6Xk/AyJFpMAuijfC8CWzrmPRv/34Whb6kcl+9A55z4E4CqAv7mPQQKALAF6sJ2MoYablj6iBckKtanm9SeAXuNJkejTIxTIQ9kkOreGRHvve1TpobAYc45aatVEaq6GivXrsyvctjwCItHddVnj9GyxHYc88feR6ObfnNKjt3nqspFyJltVx6xqXCKJGa0PptaLuO3zLDEjjEGwJoQH8Jpm2gNDMt9IAAAgAElEQVRVOp5QWZmGAXtvi7LGpbPpzvq6kGmeAK93v+3yMb70xHPJbatKE9SSA8SgFtzc86BmHCUzou+m+R4WabQ7IYrS9Y+WGTvrssbNZ+MlLWT908wZuaRBQ1m1AIArt08TbezG7FUaaV17VZk+jEEbI2DXJbBQsU+XC2CjnUMLdnT9BMCIRFe6WnNz751+3yo9OQF0v1lt81jJyaD+eDhdW3o+ndgoqdvU6L6EQaCxFFhHjLJmdGy5fVe4Phf+Sv92D09ZBRoS3a69GUiuJRGslzDogdSwtCa1nekZvPTKibi9qEkit6xwy6Vj+fhKr2nnieTpdO6wJjA6N0tQTdVkYf5DF4QfAWv5/GIit9YTFC8AK733V4VtXwXwQPTf9wP4+nAn59yPAvhrAN7pvU/cxXnsgETPZEwxNahjS31di8qrIgvFQLBnfH4Z2QGaCS09ocRZvX4rkCmiSaP+udvMZXCMm+PL3uQzRGn7NdEciS6rhlolTWiMKq8hXyEAD2MZPt+ARIfHKjmERS/rzBG3cC5t3K06p5AKswj2qHS5olLFizYt1XCcBKQ10VTYq2kTJPbIJsIhLQLG+jySOnlNw0BDYdLvQbdvWXmcKCnM9PH9+vxuPkjc/1JXXt+UNV5+503payu1fFp/503Lxlm0UW+n5bDdP/xdcUKKOjgwMqMjqccgOF2bcswaCE9CE27SmBxFna7ZbsZIBJWUAJz1HwfiQC6ZfI/68xqSruQ6gPwNeHgqoLXpJXfT42XaC2NWx/g843Kl8T5UZ0Cck2JmhRB4Ret6YahrtzAOOkd6mjo3d+S366JCSRUZBAi6AG6EwHbnr/tI7XA7KSdiSPW6rEiQLfs8G6Je7L0XOwSEcwPy2pzW7Bgg1YFRIviELJHaJlmUkqrRfOj7awhDok+VNcaCROtMOD2JQpNrWoeJ5Poeo+h6kp1102HzZ0hwiOsoqVlmvt+6qLFwsmhsSXy7F7j9PoBXOuceds6dAPg5AL8V7+CcewuA/wLAB7z31/Y5mBftXT5vY9TbQK+QjFObSFax0OngbEIL7QTC4gjIWXJNeChMzMOFQQtGh9fr10QbxFcIesMmRI1+t47Gkpq0m2xv97cU+uuci64R0bnbiTKzJlrZzpBorn4tOBnY1vWK77ueyWWJmjbLPhVJJs+cthchom4mYTANiaoN9WCECqhev5L7jwO6KJv23qyVbyVFwR+qsLe100qg0Tm44/FLLBnvIesRKIic1CanE36SkwkiVa9mDpreOgvgdG5LLbOKuhqCdZYIs7SvavUmCFrNt+trAGunA3ChKQsLgAm2NeeR1e2DFYQ67pwlSM6bI4taDwRsmhiKz0LYMEVt8IkIcCAKT9ZNECwmWRkSqOgoAHFNrP5sWEmO2sfacP+dQ6/9Z+94A5OAI72kpjmDHbKpapyoNe28ply8/6SUgbFsWvbiRHVuromiAzs0iC4rsZ4aCPXmL2gkWjTvfQnglwH87wA+A+C/995/2jn3N5xzH9ju9jcB3ATgf3DOfcI591vC6bLtQOeeyVYsaKhq3ExFNDSnl2Xt9HYFq7LCldMUNadDn8MHa0WipRY4p8sEpbLsZ0DjyWeovroua4SRbsoaV85kSlBzbit1JuUsuw6pin6Pi+5LN85xjVTzWyKkQnCuU05rt1BLiyAPRNUWFUw5t+LsBlazvFw6pEpU1kXzDJkDqNGxjjEQfIos1Ws8tZ0FyRoSvYAiylIyB09H1ywoC6tX1ZJqq0J3ULWWd02SK700aN9aoGjH76ukkr4pZFG98L1qSHQ89vD+S++rFhxtqgq3Je5Tve1QID2D5vnpgk3atzN+9q49rzTW+PgcwaKwT7Le0rkWsOme56KHtIbxFlKw6MbPX/wWvOYMR89VottsKyTXZY07lHEAISGbOs+ANUFrr3W3aUNYNkAsLMQTHVRnRRQf0ue4ovRYLJwSKJA5iCX+6xrHqrqznoi0sGkmJ1lJH14mZtfVRJPrMySVMCNYELpcyNoI61IXk2XPlzHZCtbiiiHRpf5+rBRGVO29mgjukhhyEhzQRNmIb5Z8P2KBWX0uYQnIplTOqUw3rdSOifq90M17/w8B/MPB3/569O8fPa+xvHjv8jlb+GjEdgMka1sqPeuAbX1RFnLFkOjuo+Q10XoLnOTf6ygYHQh2DAPMEZ2bqaEqFM94u9xTj7f06cbS32dV9JFoCVVJOfJdG4VpSDRTF5Up/N31mRMjZ+JDplYXxmBUQ2mR6ajqDMlmmVxhfKy9SFWr5RdlTYT+qAMhUwWrLYrCak1ZPZm6nSjPTkFKU2IqUinFRtBOaK4v01hLZW6SEg+a+v66SM+7DBEutD7RpN61oUDr37zmILFaUJN6NwnEwziB6W3ewnWasYx/T1cCozv7GwWxa5kNJCAIYzHXb0v7GPpRt0g0YQE11zLQuSXNCysSrdC5c9g0WilEc349yAolMfL55RZIHs0cfSIGGTZ15qndFViiyqI5sCJI64p85wxtpC22yPNlredKQxKeJQFoD3qCJHMkWA9iZd9K918YEr0qq0YZXenGs1g4Ve+A9ignoJyWRD/YfHa4yzNZqFGUMkeNkqQeJKvUJhLw0PqRctxWZbR92c/IiwuEEoBICFlwjMNYeyrVg5rFHoV6B/ocpc6QgCzlrAwF0VJIdPw3ycFNtbPilDELEs0yzSxbqbMbmDAXbXGlUM0ArabIVu8nLmJG4bHd0MLOWKaX0rkVqiJDUQBdfdv7QNXXEyRyAkFxXpRvbdNm98clC8OxquhwLT8brV3VSqiRKxRF702Vvo8MQS0qT5N2IpNHYQFYA1PRuXb2AI0JcbV0bqkXKaEchuscHzmRcgpw2ukq0RJsdA5DsnXN+t4bWlztknxg9aCA/pzbJIbIVLLRvdXafCYal5WE1YXFGF1bm2M3ZSNiyOZQJqrG6dqk3Ii2UJyO1KZa4cXG1igWpFvaKKrvcK0H8awUT/M/qhpqIpkpz2slh0A3dzGx2akJqlD6J/dA14NgC9sop33cweazQxA9kzW1sXoNot4TUV+QilpfkJiDwNodrWMk2pqFTfVqrfRerUA/0HYOnfrqcQKJJm0imnMTEQeadZTrY+OxDBcFDz8SxZHqHTcJtLtDjCRKER83Yxew9jMSOl8TNJQJ6XUOIHHwlCD3+EinqgHaImak4ynX15Vnp2fRfR2YJ9NQ9rAPe5+18WssA021Vushn6qDDwquI42EhNhXe0zo0anWRCfmniHzYnt425ZLYIik7iNNzClOEBMW21iQfpUmSZBbCyVcaT/U0eP1QEETeWvHWnHkliFeGm2zvY4h2boudXbHpvJwTk4MNufgQpcWWrgm0Dc8D0skanRvrUUSncNYEERqnkuClDI6t4Zk03tjrIkVk8REGKqtqT4mczgR92N9rFkQqvZwNyDROdsb9XVheyjTYHRuwf/Y0PVZT3hlI9FEjJa9H2w+siThmfK8nqB6cdO5n092uMsz2arQhYKYsFhZeZXaVLCsI3FEtEz9UElyXclBZbgWIKBIQu/BYauoeHIcorRDJJoLxhCnta0/kZWW4+vH1m+31Z+4qm3dZOzwSgtHSmymq9XWFwKJEsSEN3KQaIbEcQfORufWapL1Psi28TEkXezLnpnp1VCcboHWExTys/PYVHKLK1YG0I5PQeEZUppkWyQcj6KskwGhVnOoIblSqUntZSEhLWCX0BzWx7dQULaQEBXr2bQWYqWn/T0L0maptKhzEwcbiJkFMhoPMHEyOWHQjoWhLqR2NOwjJgXafYgK+JZWrt37JoGRi0Q7W6KDzFEsycGQyEKhSzfnJ/R31ge6rnGsrT9EWCywGKSxAUoLIhJkhe1SMsqCNAK6qKd2/nANlihlQfa+kOiirlEJHQiaO+5VYTGLLoO2hrZ06Vy6PU3y60l67f3T6NoSKyoYD6J134220CSibwebzw53eSbTqK9hu9rCamJWlrVWiq8v1ugNAg4pSx6upVFlNsJ1gvMLjGlMQ8d4FQWPjPLUHE8cXlITvVGczuCAV7XfLipxEByOC3+TxS6KRFBKs+GhLZpSZ8/7ZApoqPciEuGco32cGZ1tJTg54b8oHYuVL5iFxeTtJ0r5xbqqKJKbvnfd+dn7mErqOMfvTaiZnqqMHsavI9HyNomem7ruWgg2N+WwbVFnHU1YRqKHv12jx2oaD5KjzuqaGydFCbBJcKC9G4yGZ0FuAUKzrnhw2yrU0uSjvu5o24MoGBNBo4G4wWlcV+mETjBJz2PnfQo5CRSMdcAI1wJ4XaZG99aDGL3FlCQ8Fa7G+jg3jLPEHBdtF4/3el1nmN/FRKIyhzoYalqNSHTy/M6ZS3J4TbL2DXNdDrXPtBKksz7LANQ1iLfQ82og2THFEloKcDSJXtVygs8hEhYbvP+tP92K9snvn0bXZqUQzbs9XVOl6SpBEhTKt3mw+ewQRM9kDUo5zWkFglCRPmFl1URrPfUGyLIWVIZrNfun6ZFpOnfd36dXG9w/XwiQFgtnqnXTah6BOKOfXtCLUc/HmHpe9ccYI+gDVK6qm3pUqzp3SZxdDV0B+DOXnHWH2EHTkeRk4iZaxFJ0beecvU+0MP4bRUWR1NTxYU27Ec4/OEegh3Oqe7peMNwtW030cHvzXpWVjtIzVV1rPbrEbPFU2VRDomVKbFkHGnb8jaSTi1qbp03bIipdFpJCebW2TqF8JJUwaUoatMBbCnbl+bioapxMrGnWxHbaBJTBQQN0FGhjaR3IykmIwizA0bTSgGYzVKbp+2utidaCdTmx0CX/BoFPokfyxhAgdyj+7smQrnMEZ/twJHoBrX+3joSmv50QWDTfCEMi0+Mrtyyv5PkRtXck5TpikLxh3RtsdHBRWJPM4QBvy2ZZ3zWfMAeJVgUB4eH9lq4vJRGoOrWsju0cp+MzOnc4VgpymXBYeDeHx8ddW3SmT7rkpksgVVv/Iv3tWVgKWrxxoHOfnx3u8ky2JnTudSnX9wBcWKxxBJXzKwueH9C1U2MDugWJCaysFTRK7NUa1UQP0YlyEKAGFLM5Tm+zEPbRxlsE516sv5GzmuHetE7P9hxBvAmIEHxNuTixraur0pwgeSKmKr0GuhWnW+uLmJRNlZDo+PwSEhm2q+0zSN3huqzV+kbWA7Mpv2A10XomWQtEAd4jkn2vLLiZKsq2UepYteA3BNEx40ailWkK1ZomQ1GlvwktcCxrLyMWgqOuIU0eer2mhtAAIbAUjiWOMdAgnWoPYiU512cukaBz+w2JCrOqox0CKa4w3JxjejARKm6yaeM0WLeh4hZhN4uAXPs9KYKYJ0dOb3E0gU4bs4VYr2tdGKyG3stWLolo5zCJLtsGsWQOlUrSWJBNa16NNdHsfSP94FUkmZXwESaCloxjLaJC8ksUZqOidrpPxsq9LPfXIuwnsTgYym8R/qIoPinF4+rcDOk+hHfnYYe7PJM1dG5NRCN+qcfZpzoIDSUy2wBQea+eX1vcNTGg2AJqx0R1mm1yfTUVkgkZ/+1vHYo4BCpO2hlJZP7boEBA3uoaDvL9q7wc1IRzl1HA3GYTW/SrcxjDPtI1UnRuyRHYDJ2QwbtREbG5ZE1Te8/1QCs4cFzRXAhO2sTFQkRrJKQWaL4HyyKgUZ61TLRFeVTL5FakJV3aQeqPRXJAmTBRVXs4yM8u3G4tgQCM751vAx/NueU04HhcUrAp1duFY4bn6balx8YQTW3uS92ncA9TjlBd6w4kCxzrtixk/F0U5L0COBKtjb27joGWvP1GpSAtGHsfNEc+zDO959N+s277/72OZht6LgNAJfzm9tf54TvZfz7ebxMyZH0LZkse8HOdiIwSeQ4B+BznhQRSsFWyNKu7J2VdCWwdD7dlB0ydQ8P6sRTWj2YOlOcpTbcF4OUa3XtpWN+U46VvzKFB2pk4FK2Z1gKxXsJz/C5XdVpDAohLinQkWUskAvK3X7ZzfNrnZWyOMbNvfA5t3gn+WPP80j43axEp+aVuW9anJqCo/6DNz54CKJUiXHqwee0QRM9kjHoL6DWKbLuHU3uHWhZ36vwOtk/N8jHkuBBq4IaTRusMWjP/JEmwFKhXzTYZFe2PMULZyr4zbRlvHNiVNVQBoubc+m/SspHx2CRj76TGbmiOl8+/VNS1AcP7KIwt/uuURA/QjJ8FIpqGQTM+JVAi52fHA5bj9fHJqrwJKukg2aB/3+w7647V7rNcrw8snBPvz/i4buzS2LTfoz0H7R0af1vdOI6Tx3Xbu/GMn6E8nmZfLQERW/o+pcYgmJeFffTr7LZ9rnNYhHT4GsmDWvZdAsDRQm/rZV3bABktA/T533t+fnY8+9a1xD47P2Dwicj4JbZQ3d7fvOfNtrPfpwVSAPeVktePfrL2fCzPj73LMuuIl3JoxwNN2MmPZ+OXt7N7q7FsAP27A6SxR/Orcv3aEOTyb9eNEo6xHejc52PL876gc+4BAP81gHsA1AB+w3v/n5z3OOa2dVnh5FT/4DUhomY7m7DJR0cmDbogHi0QPkYNxQvb5W3cEUlNcNKClESDPPCprz+FLz35LD761QrAFerwaRO2xZlpzhELi3W1m663j5B9ds1vrKO/8UV8WpDbHb9b4mTn45V3cmqAbj1eo4MDFsecBIOZDqBpEdzx+PgItkimHMz4L5qToc0lLFk1dD6k+6zdf+3eTknuad++dh+1e6QluNiz3xUZ3+X4YLkOarNPfvBqGSt7p5hDDNjGys4z13Ws64kpiFbez0lJ1Oj1YnMcTUxnJnFpkE0SmWnNjs7485bLpaTjLWt9MB6kZga5U4LgyK/Lvf6U96+/huX6tNP9UK2FZrM9z7fahbGVPl5/NxnAcgiiz8fOPYgGUAL4Fe/9HzjnbgbwMefcP/be/9EFjGU2kwr9+0HyfrOyzOnmAZUdpdAcI0umvsnQ1r2/SQtSKpv7pSeexd/6x58FAFyvL2+vy5x7zSE2Oj0DqiowvlcaGjZMTKTVPaNxsUw3cQIoEq2oszbjmz5R80WCBYF5x09ZgF3PwZyORDfnZyhOHgqiifpI27Xz95zHjABxmN2XHB3t+egJL21sUwL2iUi08n6w71b77VqrQ+v5AUuCzhIgp2nn/X3OA4nOTxpYzmNLLMwzllT/9PG1CJsnGeRxZkaw/ERgXhCdFg7rxpQ7B7Nn1cxNtbxdOb5pcZQZJCd9tuj55SLhE5JT1nUAmCGQJPePXz/h/zjjtfeRJI8OoQmopO9nQ7IBQSh3h3jjYPPYud9l7/03vPd/sP33MwA+A+C+8x7H3LYmwl+AYcGhC1ImNWkHeimbALTFkU18QHpyl85pCcoBfdJcHjkcKQ6L9nt6QW0CiR5eVxpH6vnQTP2eF7FcB1hHSdgiRBw8gkLYmBXa9fXtl6gDmnvv2O+zJ72mnH8qZZuO62jR+2ak56zNE9qz0+YDaWw6Ej0N9dbOmUZgbElKliwFjOhuZpLJcg4g/zsA8hk1gG3dOQ/qOWBNcuQH/bk+RT5bKDOIzgyycpMAOXTnOb7BXLp17reXjUTn+g/ZSL6WAN0t2Ts05n9kl09mnp9+O4ea6HOxC01VOOdeBuAtAH4vse3DzrmPOuc+WpbleQ9tZ2v6zuY5tZIKZ7Ap9M/YaFYzOn5XsZge4m1BohP7SJOGxSE5Wsg1lACn5uj0yji50P17IyhUymhY6jfnLVIc7WRo7/6cqPya3z07CBTpnVITZT9/7vE8kz7dQZoaYKcocmJSSbm+5iBMKSXZR010Ds1Wu4dsnm/GlR+kWeZVrVVXu88cdO5ZKOH592Su68yBijfnyZyD6BybR6Pnibzd0bT++TPXCGX8C5eHZJ9HrX8umjoFie5ff99rWKZo4cR1Cpha82zfnpuAyvUNLYymg+Xbhd1l59xNAP5HAH/Be//0cLv3/je891e991eXy4tgne9mTC0P4AvCiSJ8BXBUMjurGV0/BxmYgjw01DbbvlOumTNZx87maeR0dK1k+uMThZQSf+f3OXeRYWjv/tDUXDp27vH5KDuhY1GkPC+BkftO5yD56rYdEWSZmaEEtsrYVcd2RxEzgATRExMNOVoDFhqeJXnGqMKWefWMfANAPuPDso8lKDUh0ZlJR2A+OvccSHQunZki0XteP9i7s086N9XEIOJwXGCRi5Xm6tiwQDA/0bzvNTYXKZ+e5GDMUfbs2PrOWSB57wY9P3k3DjaPXUgQ7Zw7RhNA/zfe+//pIsYwt62Lin6UlJpEM2N7njAj5el9ZgCbffrHa6JgJgcqkw5oVeHtq3One1FKjkfqGnSip4mRTEpRhrCMc1CdDJ642DcSPP23Wa6f6wCw+SIXRaKZcmV8w+/TKtaTumdT6pS1hOGUUhI9aO2P2aojoT2/Xd/9XQTj2LgAo6r2XKhr5ndg2ccUcM6ARJ8rnXuG8WYLd2UimblIMRVvpHRu/bvW1qfs9WOWd2m/63euX8SPz1uD+Pin3+PsbjmZ9e75egR5pRyHmujzsXO/y65Jj/9XAD7jvf+Pz/v6+7J1qTdnBwxBMqU+5TndlJoVTRpZwmKDY1O/eujs7nK+lCXvXdR/MIc6E2fD4/3a/qSDc1vRMIdtWzKhNziQj/LwRS4DoV8eEXXLmZBo4f7kZKlN12c1U5lUSo5E59WMT1U+deTaKhU5cU+mCItp90b7JiTHaVd17tCDVkNzT461MU6fqy21bLkIVrNPdA7hG+OUwbhFn/Cdmst75HmQfQvtXEpsDsR7rkDb8pvMQbI0R2a2ycsN4nId/SkdCILlBqh8/chbf0xjyGQi0CA0I0gF8nVPctbARthNS5Lk1vM7aHMSRaJ7967fRxrI9+1yS/UONo9dRKriHQB+AcC7nXOf2P7fT13AOGazsqqbPs2jSbP/AcofTbMfpy5Nn3AsTkYcFOQsQLZem7Ygmjn03fkyAxYpoHD95EL8DDfbPtHDY3ehr46vO3hnMrPdFhEo/fjBvY+ctbPMerh91URbF6mp9yacP1sYxIqiCA5ybj1im8hKnF+7N3rCK2yLVUaj/Y3tVYbvPUPCnfODbf3fNEJ+FRVTH7X502yMVHQnnVor6OCNSLR077ZjJwGyw/kJcZ0XzdrWnioP3QH4WJ1hn2Ys8rXCI2NBCmMcjK4xeBdydBOa4zN1YDKAB9bnd3x/pbVVSCAd6dtzUNLuHJl06lw6dCaSzN5PKZAMZi8HGB97ujxKJjnDn3LfzX2j/EwD6eyYACzC+x1+/8mhJvpc7NyLjb33H0EanHzB2qatjc1bMHLVvXMDqnj8Of0Np/Ta1Pux5mf+c5TNY8pZfB2xJtpIX/VwfJHLnsgza+omoH7t9tx6u1w6d6ZoCvtepyZmgumq0Pm1qDn1eFNb2KWc3vibsVKlNedqVyQc0O+V5KhzBzOHzq2h8Hy+y6Hqt/uYAr08yiJwnjTr/FrluRTLZ6OFs0ROpjgSnWNz6zoz2TQq64T0+c0u8dqzMrVln1xK8G5o6e7XZ+VmOXTuk6NFxHJJjS3v/tNSgj0H0cdEA8mGRMvt2SxdHg6Wb4e7PIOti+ZFbj668NGPP36W9WULYq7TvktWMmeBsTgzo5roXDp3bsZ0MGH2kaoIYdqO26MJohuFz/65pck9qUjOFrnMhSIH7WWLWG69U65wRq4afS5VkaI4OyOZ9mOb62uBmN7SDditJpodF/LlqcBymjr3LghTZ9I7M/493fml55BDp8txTo+PHESVxXD8DMGg9oytaLz8O+z6Gvp5Gkvez8E9sl2HzTl5gbj34b7lo+8Ad/T5updZl5nZRpD7NHnrk2b7bh/Jla/zE0z5bK09I92U7cQC2WnzfLM9k66d2/4tM4my71KKQ4ur87Hnv+z1C8BaJHp5BEBux8XqT1kNJq2zm1FpMWeBsdUwD+jcyyMxp2xDB/ZXO9QLohdNouSPv/k0vvzkszhdPjyiFO0ipETbjmW05mquuQAK7fzKIsYW+FwkNLtmab9IdO4ipT2bfi3pbsfG55AstwRick104p5J+6vtoyYg0UM13F6vaha0Jq8z3QnLUS22q3PLKMQcYl7NPnMgt9brVJPH0Ywl/zfPIWLZjGUeJDoXKeYtqvICDcqu2yPbJzcI3rdy9XkIi+U+f7WkhiTRm+OVRK5BnVxlY+05wZAtHLpn0TWewMob/8HmscNdnsE6JDpP7Y/1deM1FNMnnaMw4W0vkZOFneJk5CLbXJRt+mISFoJ4UdiUNb77XJEc9y7IG6eM5WUrtWexcA5HKhq633q5bNEaRjXNHD9LauW0qOLOJx+b/uzynHhtLlETaDt8D3riavfra/dMb0e1e5DPrpeT4GHrBDsesJXUWPZhdM056qqZQBBwfoJf50nVzmWbAPl0XJrsyaQDc+Bg+rqduzbm9gHOrTcG+DeY27Y0Zw7L7Yee+37nMtFyReuy6dx79o+Y/2OZMw+Wb4e7PIOtyyaDnlt/xGum9zepD6kpOc7GlLYoej/HfKcyz6mVqY1DJ1LL3iaFxbIzzdMz2bnKnvn1cvtdZHKVR+kim9ECgzmPub+NMhyWuvL0LqJfvW2JezalxZWOUEjnmxhES99rxpySIwJp6e85h0CWKdmZOUcANkef9bSeLXDNXCes17HcW8u1sjt25AaSmXNgDp184fQWiuzd3DuV+RxYGtljzFgD2diOFk5dxxiSulzk1rSnt/utGFduudne/aPcHus00X4I787DDnd5BluXQWCKfDR7rnFg1Bp1QRpMWDlZ2CnOjCosZhHa2WNAF45NTepWZe5mW4LOvec+0DnPKbemLJcOlYvW5ta07bPHZS6VizmQ+T20p409tc3a8i22KUj0VAq6LCw23QnKcY5NCtGZKBkwTwBsChbnCMQN95MF4s218tGbOWjwlrEAehAJGNDYPXWtCMbrTqeXsB0T4TBKRc/8RrXZLgoAACAASURBVDizL29ttuyTSwm2JFPla2euf5lJ8qlrWG1Uts9tSZvN1Mv1Tw410c8LOwTRM5gViWaZodysbk7ANEaipy8Qw4+/3mYGY4SWIdHBebDU5QB8QctBhgKF24JE67T0FEpHFoo9ItHUuaVoV/qeVttVLBeJzlWrz1bPzBTN0ZHWzCA3k0aZI4qz6zslB73TkFxpbJPp3MKz0JkErKZ9+vOz9Pd8vgSDttKd/dNeLQGLZb856pSb8xh+0xx9rTPncIqGZYqh5qB92YnAXDZPtrp3fo3+Pn8jK6PILjVjPlluInligieIYNJ67VykmHw7OeVgQH45wsHmscNdnsH66tyyUTo3XZD0cUxFj5pjtyffzjB56tz9bSGoCkGXc+MJbHi+MMFYJ4KptLcwNun3OgDh1KlJfYxED/aJ+vyF39Lpt/Pe3VSdmyySmqM/XsQGvXUzM/EsE0yD2MxA0J7lT/dipKI3O9cThj6OPkuUrDn3DkFwotdkjrL5rrRpaazj3+jboertv9LX14Po9BgcvOgsaYEbV03Wn4+GmjLVd2CeYHBKK8Ip59DmZgc/C+3VEsyPxpL8LvIQbx/tl97SmeneMUd5z+JLFm0GdbtwfgeeaOVJzLyxJ+e46J2w+UDxcx32pje815mBfA4dP6ekB5ijXGsqgBB8NuP4Bt9554/u99vIpXvzkp08DaaDzWOHuzyDBTp3fo3l+KNw0QTA1Y4H23sLguxExseWdVAan/6BS9vC7x9+/E0w2b9emADMCEN21pQ75lOR6Ho7aacWxNyJ9mx5lHQGgfxJ2I6CSEFoXhAuLQJVez/3gSRESQ/y+3IC0ePlQnxulrH1t+8eJOfUo0tzQ/wd95Sxhd+qItriNic+V1UMzai+3x/ftJrt5ljtt+XRDJ0bz5fja1iCWwsVW97HORua2o5FnKd2DDZS79Lw+QrXYpTvXMS7rHx6PJEFZtYcddPZjvjEPtPBb9ASicuFPscBjbilZLzF0LQkqnP8GQHy82kTJRn+WHuNTLo2S9aNk+jdGPq/j83P42RBbrkW9T8yv8XRvdve/6puGvjlgl655QK5LUAlQczu/Ac693nYIYiewQKdO7/mWd7uAdr3VQ1sd0QVcxAIaVsbRCfOPfzbrki0tBh1yNa0Ce80cjLSKFv/b6nzFAranU3nmhg4AHwSznXAKF1rYiAXWsrt894118/dPhx/99/ZVLE9q7ZqwYYWWKXeCfHb0uqUFw4Q6iGle6Oj5/K2o23bOut1gDyUQjuvA2cpAPnt4wD+jlj2maOVk4kSnqnEDwjziaU9YbSP/M42+4RQIzme7Xk226S7toaGuk6qfUBbIObNYawmOycI5srcuQFm3hwrbQ/Pb44yBfb9sPt/ahAhlI/dM5LMWAyZonjZNcOZPcxzWTr56tz68Vr3joPNZ4e7PIOtCisSnffRMpsizuW3i/+oJjpjgZC2hcB4OPl5uNExYYLORaKLbcAlBXTt7zeIH6V+13B8qfGWVfr9aH53ppOUIxw2W4uF9Hkk1ePu/NNqptYGJ9Ryfp4pZmr5DDEkaG4GlW7f9YC7HhuSVd08IyWf9KRUMJnS7OW+0xkJpZTp6tvTHcz0+KMEy67rQOI9mq783D+X/I2HjgX6b2n2ia4TjdW155DGantXAOn5GgLbwXWkOSWsE9b3yLKfFgSEdYvRtXmbpzw0LMeOyRzHgphc0bTcEgDJBwqgyRxzrEVEUD9eSWhmt2jKWx9z2TK5ayCt12caRdm6Jbl0ePL7DUyig+3fDkH0DNYKi2VmdRnSzOw8kehd6yKBiKId3SepJjncS0u7F0B2kFpnZKKwWPz3dJ9n+31LUa8oDf14uiPB66n2V28F5NOVJKQiBGzZ6qp0EWbf8/Tpc99UthyqObPUc2+/sx0U6NUgWvnupyDRU35vDp2b1SJqxr4bbZnovo3pKLMz7BNsDkryHAJnsyHvM/T+te6nvZObMtRt5s3hbA7UdB9y6aA530hzPHnmE5HqQKeX3qku6SIl5nU6fmixZJlzLKKpmuX0qp9Kh2/Pnxmk75tJkMsk4y1n89hguS20mB7Bwc7HDkH0DBaQaDapaO0amEkfXHzGKcI63XaOqDojMiAtDGF88TgLAaUNqPx3nyu1YXfjESacTRV6Bk5zNuJnalHX3lXVmCtgT3cepyLR4a9iH8atm8FQkhSzYlQrq42Poiz7RSIYkp4y6zcuf88MncN2e56zr9YIT0BYgmOZoheKQa9G59ZaXJ0TEq3XUbN7pCQBclsZKsdbNS0AGwomXcuTQKR3nUzaI5BXyxwOZWuAZSxzqYAD+vsVnmO21kfG+iIf21yTgQIS0heeR27NKVt/pGfJguDWJ5koXNkdn+deW4KknBZVUxO17fPLDXJZ+zbp3dwexpHy3ZFq12ME5dZE5yLd00uucpMzB7PbIYiewVokOnPS1Mzi8GiLGs96DRHVPKqPNoZ4cSvaIHcx2Lf5La++5ybTuaWscd22W5rmbDRIcJ+66HsCT3YkOo1kMycpVVvX/VPt/S2JyVkXQckJaVGS+R28eF3Y9Z3dniHanhfEp65vJYvwerC8LH6us6+WAUwQ9Nm0bBw720JHouVt0m+fUtqQep5janpifBm1qlNpfsFp0oLBgGBK70/sJFrKhyRHcUMCkfi+5opfWWyu3s3iurb9PVYVcNO1LKJsZA7i91bfnlo/wvlzkUiG5E0VdgrfgTS+kICQ5oMNEYJdFVa6tkT31jU7rInWXJZHLhuJJ5l1YIdrquhJGnGNM7Jtpo6/3c6+vSmJ7p5/w76P6YEwY7odbD47BNEzmBWJzrFcatWuTjen8e3+W8Nv6CPRHaIRZ9ZCltBeE51HexMd/cgZDvuEMafGp40jXVOdR6nKOXaqA2bti54rnpV7fA4aC+TVC04VPWFUw+74PKqc2g5vAg1/IyTDAIXOTYXFhOuLQmW7o2pJanot/5Z2fIn52Brc5L532ruxqZpvc8r8nDLpPLsg3lMRvV2qm2ztuqSAP0q8ZaJL3XjyUW8gX52Z3XstoOOJwDykjvbpnfgs2iBWYqe1Nc0kCGaJTHb9jLUb0JTRu/ueUzaSWzLEW1Cyd3daEFzVtvvLvmVK52bCd5lzBWNyMPaNZmzsB5vPDnd6BluXFZYLl3xxs/NB2w8pVwCEUbOGE94cNWhDS4mFlVXaGTtZHCX/DnTocn88LOhgE76ERHfnDb+52Dqq8fgsNNx0gJF6Z+yCOilrkYQJdOvYqJNB3gHmZOWqV3JhlN1/X3xFTXmWmRgYbK8g3ZsNQzHIsw1nZQhhslxju23Ke9H23tyBzj1V/VoWAVRqondAw6USE6C7v1OogsF4cDG9Xr5Vfp4B3QXkscbJT2Y0MJ0h4LeI7JgC7cze88EsyQWbKvnu6xpj21hntVz1aebIy0jzdrtwPEs0rosgPCkg0S2LQjp+m4iaikQbkWzJrEyAZt/U8+8z50bbAX2707cHy2WYyHR1/flu2rknL4FEhccoI0v/thilOpXAyo0Xwjn3KRh4sL4d7vQMtirqvWUdrduZ7Yps7YyQGr7+ZRtEJ8SHhurc2/OnnKMYCe7GwwK6aVnD+L6EfQKdudneP05t/ZWsqc5DFDWb2qKq7YNJMu28323eIsuEuyhbYo8oP1sgp6KNVhRD2h4czKlZfsuxu4rnSc7W1DHMikQnriOp6ce2nJBknIsmq92bNaFz72rSfWOU2NjmaMfFzMICMwW2mchvMFNywULXzXgXrOOYeixlIrFk0UR15HWhJxqZejZLVM6GRJPjWc1zrk+ZyybMZsxMPL5N0ElJEMI0CEaT9Jnq3HPNsfswqyDvwfLtEETPYOuy2n7w4+AuGPveug82fY7czJIUUDhEWVkv05SHJlPwhuPv/jtk9rrJz7djGE6ILfU7SbeszeNpzq8j0W7UNie+DxEqvB1jGSHhQ0dCy/621/DR76aOytxoaPw8JCdj64QcL5B6H7tMe/it432ci/vdpt/p0SLo4/3kb6k93oJwefk8OU4Kc4DEvuXb37U8csmxrXv3Xjm/SFXM76Gdco6tteTDYx28SEubG4lWa6JJ7/rYJJ2G3nFMFGfHWsX4HjGaX+e8y+/2XHRuSVjMKr5kmb3Yd2jRyLGg2ZfIPs4NAv7E93lpNB8Lc9tMNdq0xRVB4HMoocln6+MEsjS/ezj4Zrsy/7Ig+0R4z1dlQIrTv21FkOhgIhLdsjkmItGGBJMj25vxGYI08/pmBx7aHuUTkySVUZ1c8g9YEiQwAXvnT9wHfn19HqWMIHJ/bbb7cZ3fqHxbBzr3udnhTs9g66I21FJZJ5S05dZbS06GhFzxLOUEOvfWG0qhy+M+0cOAu7OiTATR1NmYlvVt7nufPrSJrj8OojXnP4HAs9rZjMlQbsHR/C9HQ9PHr0qbBgBDGnIRKOokZbITNJuahS+qpuPs1HvPHJw1eTZ6e7CxZkHKtPu2yzylJSKG82EcDAzHblHDHb0LLSo8HkNIIk1VD29Ov3tNt+VYy/HAfCiJhLx2rQOnf0O+nVfzEM8592FmbnFlqtE2jJlpH5AWiDmWjURnqtBL71arQZPbxzmTDryvmt/2+tnsw2nlTqxmvPUfhPm7ZQrksrFEJDog1Xn3lyZxqHDexYVPUxMcB5vfDnd6BluVdTYdm/esy1uQ5PqSsH03OreUNNAc447O3T/Ww43+drx1HjSkaJfxNteWxybWRC+PRvV/wYEEuom28v1AO32N8e9m70UWkkDobIwuJ41tXP+aHmOOQJLFclt45NhUtfdAFT5eLJLKSUEZVq7Xy6MySn3ZY8sJjGigEv1k7d3WxjBEai3ou/S8UtfZGOp9c5hBucGcrd5W2GfH+USuic5v4xOmUd7PVtnubOeg5zGa9fdmKYH3zkNKZvaIOOWWHRwfpee43vaEhcS+9M2uS71muU00ZmtmZAbhmVT73CTz1OszJL0iwotsDQrPdykkiFaFfv2NodzGsp2t4YwRNLbzo1B377Z9XTvYfuxwp2ewTVlNVuM1b8+e0HVEYVd1bimotziyyVZPAhKd+t1xEBuMJjEm1recLBetWm9qUg7XLYlYiXSNuWiXKWOLkITOWxcphpKwupzcIDr3m8uxqaJp4d2VjmdItLXeT3ZwONUx577lPtNgu2TSLe+rFLCn55dAqd/PPcr95udqn2Qx6T3yRsqnZtbSA21uD0vBXPRpZtbymqntq8bX2x+bhhmdXzODFCnh3mluSMJdNmX4fbd42jfSnZsEnvr82hZfpGSIIclyH25bkoQJx1GgJ7OPtLUV2UVYboLgYPPZIYiewdYlFxbLVUqe2o4oZA3lGsp0u6KpdEDN+Q2/MbU4jlFaeRypIDqX3iLSuZeuExpKjCXct42h7UJq0c5xdikaKqqHskXQ2ieTvNN7bOEA8PEx8a8c4+23JjoQJAvPUALW3oU5SGwbs7kSF7s4AdakT8pSx2hIdJiXpjgpOpXebrb2SXnlQ8EYGpMVRAudGYam9sU2ngPIvO/bZ2ddF22tvyxB9P5cNDb9XhSdu1ufpJpnWxu33BaHuTXLz1ckuhMOZTXd+nb5+ZGa5tq2Bsrq6vMg0Tl6M/u2fJT9ENqdlx3u9Ay2Lgx0boo058ntU9qdpLIqIdETF2/td4Ya2dS5h+MrFUd2ShDNkMPhpNM6vEcdnTutrr1Fog2KnGHRjp2X3SlDnUn3OgifyeqhzSLHA7k8pHffE/lcaNsUm5qUCf11paQZzcIbg2jp2VhEb3KQUiaEZLVdaKoboxpuylL3SQt2w7e1nOCAVX6m/rGG7yrXCbNazjdoVfiWxM2s52ifZ8a7WRrKIGKz1TtfbBCd2z1BDNJaOjZbH3S2DZvDGBOKzWP7R6Izt5PxM9dBOp51H+iCWH2Nktcw3X8oq7w1MBi9P+QGXWQv5VxldvZusu4RB5vPDnd6BluXVRYlDVAWnMyWNV27AAGJLtLtBKYiSloyoKrlyXU4vpZSmQyixzXRuUmMoYVg5/R40QqZpZz0cN+13rLB5nJc2/PRFhB6JlmkyxlbSLCAad90qLnvZ2xskZtaHtAh0enjWT0fQ5KZuvfaIJpl6bkrWbgvuc9+F5ZCYQzGUqahnCknMlzreLH7PdqQ785qcwiLTUk4pK+Tj0RTRFDZXhiQaIvaOjMr9TyYjXJv2GePich9tShidN/2eRDhMOl4C5sG2D8SnYvU5/Ykl4W/dH0WnojVhcVCGz1O59afv0jnNybxc5Po+/RPGDjCv728d4N1jzjYfHYIomcwE517IqoXWhJPFWkKToaUdZVoyFOpttrvbGuLkzXRwyBa7hVY1eMgmquf7/aqFxGiVypU7eCsBodQc15z6ctDk545E+YIToic6bdlgi9avGKfdG3mvEr13kFxWLo3RSssNi0TTal29NnL2+dA7C7inZhE527n1d1UtFthuAl07m6OmHaPgiCP5XhOdX3+INEMUdTGagluwz4MedRsY2w9F8wy11v22S8Snb5+WwIm3C8mTrhpg2Sd7ivNoS1TStjesQLykOhstG9PSH7LJJuoDj4UQh0aY2+wIHtT6f7DatQCczA+YxL/vOawfRgvL5ymqdKdP33vaiML5GDz2eFOz2CbsuaoHfloRGpL0QVzmrH6EQk13BTpmuippk0eRevMpILo/vjZQjA6PrM+d2jxfdPGEn5LYRAWm9uYOqYsHBIWsWniVsGez4tYrlEURkAiC5qFt9VwSt/zmnyvzIHRUIbuHc5Aoi/gndgVJQRszyGJRJN6Ps02IYEx0cFhc3lscycVJct5V6wIsY4y8+fIkDWLbSo9sNyXzZ14jY2JDzI6MEOKGd2X0bUluncwrhOzX7o2s6lisKymmB5PS4L0NaRjquk+qSwMpr8/Ra2ff0NKEINdZDkXM44U576701ggB5vfDnd6BmuQ6F0m5A5J3ZQ1nJMnpJD1m45Eh8U/fX4LDXkX02gopeDwhObx8QjlYCTdYJ45N7s6I23gfOTUuvJmIfZRgsA+sbvkb0n/vtQ26VprMpYVWUT1THU3hi4I18Y8zfrfwxzn3+0cUmsZ7wE4QwIi+R34/iLn5TFp8wUgO4Ar8uzTPTz9duz5CTVrndmcoUFzT3zyN0lWkEDAQUo02IWshjZ+N8Zj1KYpxjJozwHPRQdHv1u4X8o7CvBkk0Zr7Ppxp89hEWKz1ESvjX1lNWu/DcM50vP6nkx5PmzJ6xz5/jmswlAsCDtepue4EGQzOrCEZAfjSPJg+2AsdrHWac9Teq/bdphiyY0xiGTHk3Iu6fydcJtUkhSYAlKQTd6ftiSGCaPu+Hz3YWT+S5mD1xmJ8Du82+nrp5+dp6JvB5vfDkH0DLYuKyryxKgrtL5oak00mTCZU2627beuTQ4a7XJIzS0MatexzT1pxIrcWk/UIZ17rlpDi8nCH3ommLUIsYhPAZyGmWMXnWXmyqTT7p01CGNUfErVk9S5FVRuDtprZ833OL5P8y/uU+jca8O8mrqHm1APOEGUpktSTLsH7e80oAwsaTiXABx7VyzMJOlbq5Tyn2CWMpqur/AMImgvIISH/V6GFMtBlNFnEYKktuaZzGFUTDWzJpoKT2U+ayasRZPgE4XRaIeHzPW/RaKFOdDa4iq3RdlF+wia5Qrdsm47TPPkUBN9fvbCWRGex7ZW6Nxd3ZfuFNOsrvDR1a2IhBTE6xTAlTFgYtbVOys10S0tur+PTzjWbeBqDNTmpr21wU4PiU4F0c3fNsaFL7bU7zYe2IxHFBaz0rlYzRujhIbxzz9hz08V322MjCo/rT2Ya2mhx0cLFSriKAJxMEh7s/HxjoqSTbHzaCOyEeYVyzGppF/4LlNOalHHKMzw2Oa/FwuXfLbJce4wb3WI6lTn0XU1+zN9X1O7RgBaK7HmnCui0gzY0HmzUKLieK6N79g5YtDUpN/L2rSxOYbpAqxj9lzi/e7Ut/WaaNr2kySyLhqNk1gYTOzVWn7AxSV1ujejBMs10XoShSnZsyRMMC6O+PwNX9izW0qicOR4uaykedesoNvB5rPDnZ7BVkUl1zAyVNCIRHMRBh1pZkh0blaPCZg1+9jrnNlCs29rnezlkRj8A2N17mxE32Brci1O17LV2bN7fxH1evu2dhET1bFt9WLS9rplpuxHWCyY3N5MfneYIN0Uy1Wittiu+glAhyhr40s54cX2OA1JkIMTrpugOf5WwT/NWmbUTCwSNgeo7akqnTYanpEqLGaYdzfGMgU14DcqQj+fjKFZkrgRFYYiHQbYHMjUt9dkezDaHWKP61OOsRI6lhgKibCpwmAro/o1YzcycSz5/cljYwW7aGFTzcR5IjA2hQRQVevxQmGMJy6yfdf/3+xwpzPNe49VUdOevKKIRlsfRJQqhe1d/YqUNWZKmrsjqCmztJlhqHj6fBdD2emQaCYsFujc+aJMVmv7OE5chBn7IdhFCoftW8BH+u3t90KYI4zKlttjW2z/YUSLxZpphQ5uKUnYVaDvPB2dXQKcTcUTSSknvA0gFCdFcmA2FUeYtPHMkfBcE1HBuU2lYjPaqSH4tVD5rTXRWkJlrehiPF9NDoK2iRRBHNGKROeydUTgwBhkPZ+DKM00jRWAz/El8TWsLaymql+H58MCtVwkOpeOv0+TE1TN/0r+S/CB5XpyHYwqKYCiK9sfbH57Yc5CzyOzijhQJURxQdFb5jCnPdQWyx/l7lRI7TpagLyLE1iQjNy+rQ2ij4/UiSuMr6tzmm+8LJBiE6m4CNf6OxXsIhzGoOOxbySaZvHJ98zas1B1TnLvJRRFY0XEJiUBNGbM2qAevetzkeYDVhM2xXZS554owMKUibVzMoQO0Gl4IRjMqWfOEUbbxSpDq5U2MJVopYbAtXU6LTXRVO1Z0fPYA0tjX9YJspEghsxhYhBe25Bo6RtnTKgALLCE3QsVbWPMGRZIMV0M3qHB5oeJ5WLGNn8imkp82mAXTcfXjOkFsN8+VdlcLoHZbm/1CF6Y38YL0Q53OtPYIm5FBSk1iiBjU+tbguUGqyG7faZMjDvRuQ1Uvn1arM4dzNLfek4keqq4VJeYmS76A1xQu6JzEmhjfZZF9W3aY9uW/MltQcGFS3ZPkIRaRu3eW8fdsk6E/feBIu3CpmF1fZIVtR6AAHLw0CHR42t6Q9DZah1kULHZ+zuXbaIkpGSs3y+b5+LrWHpJS8mHkji+AHeOn0/GkE6GBLKyjoIEUUzHZdWiZfoc+mKt62SlXyuSBGdBMFuDLN+Vtj0kaSjdXtheGdsEPl/p+AAPYmVle/btEGVzVgpwaHF17na405m2ItQYJuJEa6JpOwG2YNqobLn0WUsPU6ZKGRtzBPZtKacpNXENJ/o5g2hG15pahx+MTbRae5p9mQWtyzGG0qwJ5ZYlvaxsi0Wmg5CbXErNV1rCr24dJ9u4RZQxk2mgXV9y2lL3ekMopZK1VNjEca1zKNG5CzlotDiW1u9as03bo3W/82rHauDvC3NIVTq3qcWVzfHUko5zaYech7HEeleSMK1kJHwDUh9ndj87dWcp2fTiDgSYOjXz6bh6ti3JznwVdv/ZWsASuc/nJInESqlJezLGnukYULrmCkuAMdBuH0yvg6XtQt5i59xPOuf+xDn3mHPur17EGOYyRr2hdO1Sf+nb7VK7CBLEl8ZgNJc6U1UGR2QHKuGuLa7mtiLxeyzozRyBZ4viTXQuWR1+sNxAbh/WZWL3MzYNDQQ62iZDmnmP0/2+t7ntV1Lj094r9k4Ora27HwSUG4JQM9McOwm5SM1tbauqHec9rR6wm8uFd0u5hxYEoRPImv5tbAyU/TlsTdDG2KYKJAFxslMTtLTVoJ4c71fU7byMIZXhWxZbXBFHvqRomu6zsHejLel5kQYCDE1kftKKiNxZhUVz3+VcOv3zMUkSRNsYS4KCViKSrAtTMh0IVo7TKtu/yOncLIZ0zp065/677fbfc869bF9jOfc77Zw7AvB3ALwXwOsA/Lxz7nXnPY65bEXqh26QCa91yoSPiilh3tjomalAmbtEso67UGdSu27nFjUDxnpSd/L+i3bfSycXjERHk9V50YvCIic9M5apb4+/oHunWecgkbHvic7N7g1DYVjS68amUs//fLHUu6yJbW0MQV5v/+CoDe5ji2LtGAhWE9CpoISeelaWetuUaU4ME4EsFZpqix5pNcSlbS7XbGUIGMM8nBPE7ILcSsFcV7vLn7mmRcFKC1rFdcXxbNG759F3LS1HVrFRKeEb3hG2/jAkVEwCV3qQHezFWtdZEiTa2haViUdKzy88n9x3OTcJf/Q8TOKzem1ePqmXRHWtW6f5P4XY4qp//EV1NzkPM8aQvwTgO977VwD4WwD+w32NZ7mvEyv2NgCPee+/AADOud8E8EEAf3QBY8m2zY3v4urlT+PudQXcOOtvXF3DA8U3cfXyNdzlnwVwqfn7M48Bz34FAPC9/k/wnZNvYrm4v9l2/fPAc19rT3HP4qv4OraOWfFd4NpHepe4d/MFXL38TTxY3QCufb1//epG+8/j7/wu4KIPq3gKAPDQ6Tdwz/G3gWu3AvUKAHDHcnwdAPBVhauXP90sntdu7W2rt67XXfVjwLUyOqgAAJy5NWrfvG43Xf8DoDxpr3fL0bPN9dYlrl7+NG65dAy/aQ4//e4/74+73ozGdWVxIzle1GsAwK1H14HiOnB6Nt4HwJ3L7/SP9xW2fhcurb/Q/f3aRwBf9g++9pH+mIbjKK8DAG4/eqrbtn0udx0/0fwtek549ss4+s4XcfXyp/G9V64Az1Tdtue+CqyfwN2rr+Dq5a/gZf4p4Nq3gfLZbp/Nk3jjyefw9OUCV566AqwGn/j6CVy9/Ontv1/b3/bkHwDuqNt+7VbAV/19Hv+n3fbrp9tr9t+Xq5c/jSNXA3h984fvfAJwTErckwAAFD5JREFUzTj89hnfdLYErt28vR/Nc8LTf4ybnn4aVy9/Fq8+fimAu3vjis/fjm9kHq89+1MsFsfb8Rfba6yAax/B8smncfXyp/HaS7cA17aLePT8br3xSVy9/GU8uLgLwO3NH7f3HQAeqj+L1519C8uje7dj6D/vVyw+heLy07j9hgOu3dIf2vY5nbgied/E3xaeb/F0t/2Jm7vt3/nD9v6225+Mt398vL13/meA41vbBMdt608B17o5CIh7h8uL8y1HzwLPfrH5aVtn4YHjb/S+m5DUe+Dk8fG3sn3Xjl05+s5Xha5Ie4S6f77qRldzm2DHhN9za/01AON54WyxTs4pbYuyRIC52oQETXpZDcHp5ac/BlSXmrnhqPmGQpB228l4fuvGXGMB4LYbnwSufal7twd289Fzg/msm7PK7T/vX3wRuBY9y+hcgb30ssvfab5dwS6P5t26/VdwFu9ffq3bZziXBGvPUff+HJRu7yw/B1yL5sh4vtza8sl/hl7f7vI6cNQ81/BeX3rmDwBc7u+zvNy+Cw+cfHP0DrX/3J7k8lO/D1yPn++4M/TtqfWzfBYQ1LAle8nRM8D17fqzutae0xfNHHr55Ai4cdxsf+4rwPrbAICbnv42rl7+LF5//C3g2rp/0uq5lpLaJi+e+jTw9GfbXV69+EOcXX4Otz17DFy7PDj+Bqra4/bld3EJTwM4BW58vZnLtvaqk8/hufoGlkd39MYd7I3Hn8R3zp7D0eKVzR+EOdBV2/Vp4BO1c9hT0dgG60/zt8H6sH2e9xw/0fgE7fbovbv++fHx8Zp/7SP97XV0f5/4PQALen0Pj0cufwb3bqrmOx7YqxafQnX5abzkucQaAuD2576Gq5e/hFe5p4Bn72z+WHbf/GuO/hA3Xb6BW69Hz698Zvtbarz57JNYHdW49J2bgfAOhHe9WqfHX1xv//nQ6Te7vz/7RWD1rd745PW5RtKe+hTw9LF+fDR33Hv8bdx29Ex/e/QcXnv2pyj9Ubd964OhXrfPr12DAeCJf46AJ7pnm99//ykA3NXMi9G7dRL8h7PIfwDa+1fWHjcdPYdXLz4BXHsqGl8zrxd1jftPvo3T5Uubv1//AvBc57fftfoyrl7+Kr4XzwDXHh+tg7X3ePnpV3Fv8VHgxuW+b/zc1/CKxeexOHkCJ8uHt/cw4RcvjoE73j7++wvHLDHkBwH82vbffx/Af+6ccz6Ij8xoFxFE3wfgK9F/fxXA6Ik65z4M4MMAcHJycj4jm2Bnx0u84f7bcduVy8DxTcDJS5oNVx4C1o/j57//MtzREj/wqvuA5TboiCblf+2HXoErZ2d406te10xoAwfh3/zRN+G3PvkkHnrozcD6G6Pr/8tvexglFnjna+4dL9Knt+NX/qUP4JN/+kW4o5PRNqwex19531vxp48/CyyWwOIm/Mxb7sMrHny4+e+Bnbgj/MwjD+EHX3HHaPtPPfJ63NjUeM8bHh6MY4mfect9eOUDD+BsUeJjX/garly61KTRFzfhQz/wMG6+9W5gscSVsyO8/y0P4l2vvhPF6ml86ivfGo97scSff/dr8K3njvH4k9/C99x2BTffcldyvDi9Az/zg2/FfbeeAKcvAS7fN9rlxx59O77/Pj84fom/+N5H8Lt/+iQuXboFv/Le78Pnnii3+yzxr//Qw3js2nXc/dLtfVgs8e/89CP448fL8ThObsPPPPoqvOzuO9ttbnEz3v+me/HGV7xye/zNzf+WzwLHN+OhOzx+8o334/1v/h5gedYskABw3ARG733TA/jW9Qof+L4Hm+NOX9rsUz4LnN6Bf//9l/BPH3scV85Om/t8fEszGR/fAsDj1z74Zjx27Rng9I7G8Tq+pVkstg79r/70G/Ctp9ft78XipcD6yWbyXRzjz7/r1Q3SurwJKG8AZ3f2fvKfefRhPPLgbcDJJQA1sOie4ZWzI3zgLQ/ina/q7gdOb2+C1OVlvO3lp3jPN67jzz76IHDsAfh2XME+9APfi1fcdXPiXt+K5eYpvO8tD+MdL7+zG39xvblH7givvPsl+Ik33IcPvuX+7vjFsnG43RHe/foCX/xuiZ999GXA2Vnzm6Nv8l99xytwenKCRx56KXBy02gMv/zu1+B/+cOv45V3vwQYIi1nd+Iv/vhr8MVnzprF7/R2DO0v/PhrG2QsPu/JrU0S4Oise3aLk2au8WUbLIRnd+3p9Xb7bYCve/fv1z74Znzu2jOD878EuPQ9+Os/+xr8o49cxyvuuWM09oe+5yG887XX8e4f+dnRmAHgz737HXjw0nebd/TSvXjn3Q/gjz/7Jnzo0bu657C8godvuQ+PvuGt+OAbbkl8s0t84Pu+B69/8P7m3bx0b7vl/rvuwKNvuIqfePt48f8b/8p78MQ3P90/3+Jm3HrnvXj7m0/xnkfeOjrmL7//7fgHv/0k3vSy+4DlSXOPt/Zv/fQ7cbb5cnJO+SvvewR/+KVv4ejyPcDZXb1tb3v5Xfj4a9+En3/kLuD0ztGx/9GH3oX/52NHuO2mm5rv8uS2dr+3vfxufPy1b8bPPXIHcPlm4OjS+Hf+mR/G//m7GzxwR3i3ls23u+wSJr/4nh/CPcdPjOYzLG8Cjm/Bh374tSif/ize++b7EvvcDBzfjFtPlvjBNz+Kn3jVWfPuLO9pxhrZh3/yR3Cb/+b4Hp3dDZzejh99/Rk+//k34hcevbvb5+wu4Oyedtdf/3M/hi9/6eP9c5zd3Tx7AL/842/Bf4uv4h2veaC/piyadwxugb/9iz+Ozz/2+813GtvJbe3z+fU/+y585GMOt99ycx++PbkNOLsHb7/9bnwi3PvBO4RL9wBugf/sF38Mn/jUP8PyOJGIPburnQN/6u3fj6v3ptaBW9vf9a63vxNvfPCe4Vl69i/+8Dvw6tue7ebYaK44OznCBx95ED/8yjuB4ytA9Xi7NgDAo997F97z+uv4+UcfHI9jcQve+dbvx2PXP4OfeuRuAM+M3rW/9OOvw//x6W/hvpfcPJ7DTm/HL737rfjfPvU4XnbPfUD59HZdQTOG8jn8u+//Pvzfn3scN50uO38osl993xvw23/8OF565aSdl2P7D372zfjU155u7un6ieYeRPYrP/E6PLuumnFXG2B5pfc7/41/4dW4cno0/u2ntwPVCr/wI2/EbZdOxu+d///au/dYu8oyj+Pf5/T0dLAFWrxgaaktHbxgUVoqQbwkioGCcnFErRpg1NF4gWgMRpTEqImJl+gfRpQwkYwaL4gzZuokRskMGROjIGK5dAq2BRwrtUirUCmXlj7+sd5T1jnsfc5mn3322uf0+0lWztrvXmv33fu337XWu9e7VvdDDJX1h2vPD1c/9A4fBkPDXHjaSo571oKD+36G5pXvX5Z///nMnze3xWdfta83nbaI/XPv5dSVR0OLs+2XnP5Cfnxbm30IcOaJS9ixZx/nrlkGc4er73TtM7ps3Yu5/v92csyi2vd9ZFG1n3jiMT59/mHcdPcuhofnPvn80OFVDsPP4BOvX8WfDu7/Rz+7o6qc9+3h4+esZfOf98GC5U92UGvGHj+M8w9Hc+n5r+ee+/4AR7yw6oCPOzFx2boXs+fRfS22T8+Fobm857WlHzSmrZZtXAzxxrWPsnrZwiefH1lI9QNbtd5bT1nBiUuPhIX/CI8+QL1z/8wFc3jDSct43YueAyPDY45dAI5/bovjh9HPb+6RnL7mMP5/914ueNnyp9ZvZCGvWX0Mv//bZs44eTkMPQ7DY3+kOmf1MnY/coCzTlz65PdrtP4H9o397swZgpGyXViwAvY9ePD4Y8XiJdWxY4sf+VrmMniGI+Lm2uOrM/PqMt9JH/LgMpm5PyIepDor8kCvKxrT0DGf+B+MeDNwZmb+S3l8IXBKZl7abp358+fnww8/3O5pSZIkSdIMFhF7M3N+m+cm7UNGxKayzPbyeFtZZlev69rEwPntwLG1x0uB+9osK0mSJEk6tHXShzy4TEQMA0cCu6ejMk10on8NHB8RKyJiBFgPbGigHpIkSZKkwddJH3IDcHGZvwD4n+m4HhoauCa6jE+/BPgpMAe4JjM39bsekiRJkqTB164PGRGfAW7OzA3AN4BvR8RWqjPQ66erPn2/JrobXhMtSZIkSbPXRNdED5rZ+5+JSZIkSZLUY3aiJUmSJEnqkJ1oSZIkSZI6ZCdakiRJkqQO2YmWJEmSJKlDdqIlSZIkSeqQnWhJkiRJkjpkJ1qSJEmSpA7ZiZYkSZIkqUORmU3XYVIRcQB4pOl6TGIY2N90JdSS2Qwusxls5jO4zGZwmc1gM5/BZTaDq1/ZHJaZM+Ik74zoRM8EEXFzZq5tuh56KrMZXGYz2MxncJnN4DKbwWY+g8tsBpfZPNWM6OlLkiRJkjQI7ERLkiRJktQhO9G9c3XTFVBbZjO4zGawmc/gMpvBZTaDzXwGl9kMLrMZx2uiJUmSJEnqkGeiJUmSJEnqkJ3oKYqIdRFxV0RsjYjLm67PoSAijo2IGyJic0RsiogPlfJPRcQfI2Jjmc6urfPxktFdEXFmrdz8eiwi7o2I20sGN5eyoyLi+ojYUv4uKuUREV8pn/9tEbGm9joXl+W3RMTFTb2f2SQiXlBrHxsj4qGI+LBtpxkRcU1E3B8Rd9TKetZWIuLk0ha3lnWjv+9wZmuTzxcj4s6SwY8iYmEpXx4Rj9Ta0FW1dVrm0C5rTa5NNj3bjkXEioi4sWRzbUSM9O/dzWxtsrm2lsu9EbGxlNtu+ijaHz+73+lGZjp1OQFzgG3AccAIcCtwQtP1mu0TsBhYU+YPB34HnAB8CrisxfInlGzmAStKZnPMb9ryuRd41riyLwCXl/nLgc+X+bOBnwABnArcWMqPAu4ufxeV+UVNv7fZNJXv/5+A59l2Gsvg1cAa4I5aWc/aCnAT8PKyzk+As5p+zzNpapPPGcBwmf98LZ/l9eXGvU7LHNpl7dR1Nj3bjgE/ANaX+auA9zf9nmfK1Cqbcc9/Cfhkmbfd9DebdsfP7ne6mDwTPTWnAFsz8+7MfBz4PnBew3Wa9TJzR2beUub3AJuBJROsch7w/cx8LDPvAbZSZWd+/XMe8M0y/03g/Fr5t7LyK2BhRCwGzgSuz8zdmfkX4HpgXb8rPcudDmzLzN9PsIxtZxpl5s+B3eOKe9JWynNHZOYvszqy+VbttdSBVvlk5s8yc395+Ctg6USvMUkO7bLWJNq0nXae1nasnDl7LfDDsr7ZPA0TZVM+27cA35voNWw302OC42f3O12wEz01S4A/1B5vZ+LOnHosIpYDq4EbS9ElZcjJNbUhPu1yMr/pkcDPIuI3EfHeUnZ0Zu6AaiMOPKeUm01z1jP2QMa2Mxh61VaWlPnx5eqdd1GdaRm1IiJ+GxH/GxGvKmUT5dAua3WvF9uxZwJ/rf1YYtvpnVcBOzNzS63MdtOAccfP7ne6YCd6alqN8/d2530SEQuAfwc+nJkPAV8HVgInATuohgxB+5zMb3q8IjPXAGcBH4yIV0+wrNk0oFzfdy5wXSmy7Qy+p5uFGU2jiLgC2A98pxTtAJZl5mrgI8B3I+IIzKGferUdM7Pp8zbG/nhru2lAi+Pntou2KLPtFHaip2Y7cGzt8VLgvobqckiJiLlUG4DvZOZ/AGTmzsx8IjMPAP9KNVQL2udkftMgM+8rf+8HfkSVw84yzGd0mNb9ZXGzacZZwC2ZuRNsOwOmV21lO2OHGptRj5Sb6LwBeEcZskgZKryrzP+G6lrb5zNxDu2yVhd6uB17gGrY6vC4ck1B+Tz/Cbh2tMx203+tjp9xv9MVO9FT82vg+HIXxxGq4ZEbGq7TrFeuqfkGsDkzv1wrX1xb7I3A6J0hNwDrI2JeRKwAjqe68YH59VhEzI+Iw0fnqW7CcwfV5zp698aLgf8s8xuAi8odIE8FHixDiX4KnBERi8qQvDNKmXpjzNkA285A6UlbKc/tiYhTyzbzotprqUsRsQ74GHBuZu6tlT87IuaU+eOo2srdk+TQLmt1oVfbsfLDyA3ABWV9s+mN1wF3ZubB4b62m/5qd/yM+53uTPXOZIf6RHXnut9R/Xp2RdP1ORQm4JVUw0NuAzaW6Wzg28DtpXwDsLi2zhUlo7uo3SnQ/HqezXFUdzi9Fdg0+plSXWP238CW8veoUh7AleXzvx1YW3utd1HdAGYr8M6m39tsmYBnALuAI2tltp1msvge1XDGfVS/4L+7l20FWEvVkdgGfBWIpt/zTJra5LOV6lrA0X3PVWXZN5Vt3q3ALcA5k+XQLmunrrPp2Xas7MtuKnlfB8xr+j3PlKlVNqX834D3jVvWdtPfbNodP7vf6WIa/UJKkiRJkqRJOJxbkiRJkqQO2YmWJEmSJKlDdqIlSZIkSeqQnWhJkiRJkjpkJ1qSJEmSpA7ZiZYkqQ8iYmFEfKDMHxMRP2y6TpIk6enzv7iSJKkPImI58F+ZuarhqkiSpCkYbroCkiQdIj4HrIyIjcAW4EWZuSoi/hk4H5gDrAK+BIwAFwKPAWdn5u6IWAlcCTwb2Au8JzPv7P/bkCTp0OZwbkmS+uNyYFtmngR8dNxzq4C3A6cAnwX2ZuZq4JfARWWZq4FLM/Nk4DLga32ptSRJGsMz0ZIkNe+GzNwD7ImIB4Efl/LbgZdExALgNOC6iBhdZ17/qylJkuxES5LUvMdq8wdqjw9Q7auHgL+Ws9iSJKlBDueWJKk/9gCHd7NiZj4E3BMRbwaIykt7WTlJktQZO9GSJPVBZu4CfhERdwBf7OIl3gG8OyJuBTYB5/WyfpIkqTP+F1eSJEmSJHXIM9GSJEmSJHXITrQkSZIkSR2yEy1JkiRJUofsREuSJEmS1CE70ZIkSZIkdchOtCRJkiRJHbITLUmSJElSh+xES5IkSZLUob8DQSYwA140IwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### CYCLE vs LABEL DISTRIBUTION ###\n",
    "\n",
    "df.Timestamp.plot(figsize=(16,6))\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Timestamp')\n",
    "plt.twinx()\n",
    "plt.plot(df.Labels, alpha=0.3, c='orange')\n",
    "plt.ylabel('Failure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471
    },
    "colab_type": "code",
    "id": "Or6-StUBoD2v",
    "outputId": "06f57313-8e16-4d5c-d004-45b53bfb72ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'standard deviation')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ji7S+ntyVQAEBdgIu657fALgA2GXu0UmSJEmSNIN1Fp6qql2q6mbAx4EHVtX2VbUd8ADgA0sVoCRJkiRJ05qmuvJeVfWRyUZVfRS4+/xCkiRJkiRpcaZJcn+U5AVJdk5y0yTPB3487S9IsnmSM5L8T7e9S5LTkpyb5JgkW3b7r9ltn9e9vvOCn/G8bv85Sf5ktkOUJEmSJF1dTJPkHgKsAD5IW0boht2+aR0OfG3B9iuAV1fVrrR5vo/v9j8euKyqbgG8unsfSW4DPBK4LXAg8IYkm8/w+yVJkiRJVxMbTHKr6tKqOryq9ugeh1fVVJWVk+wI3B/49247wL2A93VvORp4cPf8oG6b7vUDuvcfBLy7qn5VVd8GzgP2nu7wJEmSJElXJxtcJzfJbsCzgZ0Xvr+q7jXFz/9X4LnA9brt7YCfVNWV3faFwA7d8x2A73Y/+8okl3fv3wE4dcHPXPg9kiRJkiRdZYNJLvBe4E203tjfTvuDkzwAuLiqVia5x2T3Wt5aG3htfd+z8PcdBhwGsNNOO00bpiRJkiRpRKZJcq+sqjcu4mfvDzwoyf2AawFb03p2b5Bki643d0fgou79FwI3AS5MsgVwfeDSBfsnFn7PVarqKOAogD333PP3kmBJkiRJ0vhNU3jqQ0mekuRGSbadPDb0TVX1vKrasap2phWO+nRVPQr4DPDQ7m2HAsd2z4/rtule/3RVVbf/kV315V2AXYHPT3uAkiRJkqSrj2l6cieJ53MW7CvgZov8nX8DvDvJS4EzgLd0+98C/EeS82g9uI8EqKqvJHkP8FXgSuCpVTX1sGlJkiRJ0tXHBpPcqtplY39JVR0PHN89/xZrqY5cVb8EHraO738Z8LKNjUOSJEmSNG7T9OSSZHfgNrS5tQBU1TvmFZQkSZIkSYsxzRJCRwL3oCW5HwHuC5wImORKkiRJkpaVaQpPPRQ4APhBVT0WuD1wzblGJUmSJEnSIkyT5P6iqn4HXJlka+BiFl90SpIkSZKkuZlmTu7pSW4A/BuwEvg/XMJHkiRJkrQMTVNd+Snd0zcl+RiwdVWdOd+wJEmSJEma3QaHKyf51OR5VZ1fVWcu3CdJkiRJ0nKxzp7cJNcCrgNsn2QbIN1LWwM3XoLYJEmSJEmayfqGKz8ReDotoV3JqiT3p8Dr5xyXJEmSJEkzW2eSW1WvAV6T5K+r6nVLGJMkSZIkSYsyzRJCP0hyPYAkL0jygSR3nHNckiRJkiTNbJok94VVdUWSuwB/AhwNvHG+YUmSJEmSNLtpktzfdl/vD7yxqo4FtpxfSJIkSZIkLc40Se73krwZeDjwkSTXnPL7JEmSJElaUtMkqw8HPg4cWFU/AbYFnjPXqCRJkiRJWoT1LSEEQFX9HPjAgu3vA9+fZ1CSJEmSJC2Gw44lSZIkSaNhkitJkiRJGg2TXEmSJEnSaJjkSpIkSZJGwyRXkiRJkjQaJrmSJEmSpNEwyZUkSZIkjYZJriRJkiRpNExyJUmSJEmjYZIrSZIkSRoNk1xJkiRJ0miY5EqSJEmSRsMkV5IkSZI0Gia5kiRJkqTRMMmVJEmSJI2GSa4kSZIkaTRMciVJkiRJo2GSK0mSJEkaDZNcSZIkSdJomORKkiRJkkbDJFeSJEmSNBomuZIkSZKk0TDJlSRJkiSNhkmuJEmSJGk0THIlSZIkSaNhkitJkiRJGg2TXEmSJEnSaJjkSpIkSZJGwyRXkiRJkjQaJrmSJEmSpNEwyZUkSZIkjYZJriRJkiRpNExyJUmSJEmjYZIrSZIkSRoNk1xJkiRJ0miY5EqSJEmSRsMkV5IkSZI0Gia5kiRJkqTRMMmVJEmSJI2GSa4kSZIkaTRMciVJkiRJo2GSK0mSJEkaDZNcSZIkSdJozC3JTXKTJJ9J8rUkX0lyeLd/2ySfTHJu93Wbbn+SvDbJeUnOTHLHBT/r0O795yY5dF4xS5IkSZKGbZ49uVcCz6qqWwP7Ak9NchvgCOBTVbUr8KluG+C+wK7d4zDgjdCSYuBIYB9gb+DISWIsSZIkSdJCc0tyq+r7VfXF7vkVwNeAHYCDgKO7tx0NPLh7fhDwjmpOBW6Q5EbAnwCfrKpLq+oy4JPAgfOKW5IkSZI0XEsyJzfJzsAewGnAH1TV96ElwsANu7ftAHx3wbdd2O1b1/41f8dhSU5Pcvoll1yyqQ9BkiRJkjQAc09yk2wFvB94elX9dH1vXcu+Ws/+1XdUHVVVe1bVnitWrFhcsJIkSZKkQZtrkpvkGrQE951V9YFu9w+7Ych0Xy/u9l8I3GTBt+8IXLSe/ZIkSZIkrWae1ZUDvAX4WlW9asFLxwGTCsmHAscu2P+YrsryvsDl3XDmjwP3SbJNV3DqPt0+SZIkSZJWs8Ucf/b+wJ8DZyX5Urfvb4GXA+9J8njgAuBh3WsfAe4HnAf8HHgsQFVdmuQlwBe69724qi6dY9ySJEmSpIGaW5JbVSey9vm0AAes5f0FPHUdP+utwFs3XXSSJEmSpDFakurKkiRJkiQtBZNcSZIkSdJomORKkiRJkkbDJFeSJEmSNBomuZIkSZKk0TDJlSRJkiSNhkmuJEmSJGk0THIlSZIkSaNhkitJkiRJGg2TXEmSJEnSaJjkSpIkSZJGwyRXkiRJkjQaJrmSJEmSpNEwyZUkSZIkjYZJriRJkiRpNExyJUmSJEmjYZIrSZIkSRoNk1xJkiRJ0miY5EqSJEmSRsMkV5IkSZI0Gia5kiRJkqTRMMmVJEmSJI2GSa4kSZIkaTRMciVJkiRJo2GSK0mSJEkaDZNcSZIkSdJomORKkiRJkkbDJFeSJEmSNBomuZIkSZKk0TDJlSRJkiSNhkmuJEmSJGk0THIlSZIkSaNhkitJkiRJGg2TXEmSJEnSaGzRdwCSpA3b+YgPz/13nP/y+8/9d0iSJM2bPbmSJEmSpNEwyZUkSZIkjYZJriRJkiRpNExyJUmSJEmjYZIrSZIkSRoNk1xJkiRJ0miY5EqSJEmSRsN1cqU5mfe6pq5pKkmSJP0+e3IlSZIkSaNhkitJkiRJGg2TXEmSJEnSaJjkSpIkSZJGwyRXkiRJkjQaVleWJEnSRpv3qgLgygJXN55TWix7ciVJkiRJo2FPriRJV0P2kEiSxsqeXEmSJEnSaJjkSpIkSZJGwyRXkiRJkjQaJrmSJEmSpNEwyZUkSZIkjYbVlTtWmZQkSZKk4bMnV5IkSZI0GoNJcpMcmOScJOclOaLveCRJkiRJy88ghisn2Rx4PXBv4ELgC0mOq6qv9huZJEmStLw4DU+b2tDOqUEkucDewHlV9S2AJO8GDgJMciVt0NAuzFr+5n1OeT5JkrR4qaq+Y9igJA8FDqyqJ3Tbfw7sU1V/teA9hwGHdZu3BM6Zc1jbAz+a8+9YCmM4jjEcA3gcy8kYjgHGcRxjOAbwOJaTMRwDjOM4xnAM4HEsJ2M4BhjHcSzFMdy0qlas7YWh9ORmLftWy86r6ijgqKUJB5KcXlV7LtXvm5cxHMcYjgE8juVkDMcA4ziOMRwDeBzLyRiOAcZxHGM4BvA4lpMxHAOM4zj6PoahFJ66ELjJgu0dgYt6ikWSJEmStEwNJcn9ArBrkl2SbAk8Ejiu55gkSZIkScvMIIYrV9WVSf4K+DiwOfDWqvpKz2Et2dDoORvDcYzhGMDjWE7GcAwwjuMYwzGAx7GcjOEYYBzHMYZjAI9jORnDMcA4jqPXYxhE4SlJkiRJkqYxlOHKkiRJkiRtkEmuJEmSJGk0THIlSZIkSaNhkitJkiRJGg2T3KuZJJsluXPfcWwKSW6a5I+759dOcr2+Y7q66c6nh/cdx6aS5C5JHts9X5Fkl75jmlWSbdfyuEbfcU0ryeZJ/rPvODQe3Tn1z33HoVWS3HEtj5snGcSqH9059b99x6Hx6M6pZ/Qdx8ZK8+gkf9dt75Rk715isbrybJK8di27LwdOr6pjlzqexUhySlXt13ccGyPJXwKHAdtW1c2T7Aq8qaoO6Dm0mST5ELDmH+HlwOnAm6vql0sf1WySnFBVd+s7jo2V5EhgT+CWVbVbkhsD762q/XsObSZJzgduAlwGBLgB8H3gYuAvq2plf9FNJ8nHgQdW1a/7jmVjJPkD4B+AG1fVfZPcBtivqt7Sc2gzSXIF675OPauqvrX0Uc0myaeBA2rgNz1JrgM8C9ipqv6y++y7ZVX9T8+hzSTJqcAdgTNp16ndu+fbAU+qqk/0GN5UkhwH/HlVXd53LIuVZDfgjcAfVNXuSW4HPKiqXtpzaDNLchbrvk69tKp+vPRRzSbJ8VV1j77j2BhJ3gj8DrhXVd06yTbAJ6pqr6WOxZ7c2V0LuANwbve4HbAt8Pgk/9pnYDP4RJKDk6TvQDbCU4H9gZ8CVNW5wA17jWhxvgX8H/Bv3eOnwA+B3brtIfhkkmcnucnC3sO+g1qEhwAPAn4GUFUXAUMcHfAx4H5VtX1VbQfcF3gP8BTgDb1GNr3zgZOSvDDJMyePvoNahLfT1ne/cbf9DeDpvUWzeK8CngPsAOwIPJt2fXo38NYe45rFGcCxSf48yZ9OHn0HtQhvA34FTBqqLwQGl5DQ/sb3qKo9q+pOwB7A2cAfA//UZ2Az+CVwVpK3JHnt5NF3UDP6N+B5wG8AqupM4JG9RrR4HwU+DDyqe3wIOAH4Ae1aPAQnJfl/Se66cJRD30HNaJ+qeirt74OqugzYso9ABjEsZJm5Ba114kq4qsXiE8C9gbP6DGwGzwSuC/w2yS9orahVVVv3G9ZMflVVv57k6d0QpyG20O+xRi/ohyY9o0m+0ltUs3lc9/WpC/YVcLMeYtkYv66qSlIASa7bd0CLtGdVPWmyUVWfSPIPVfXMJNfsM7AZXNQ9NmOYDQ0T21fVe5I8D6Cqrkzy276DWoQDq2qfBdtHJTm1ql6c5G97i2o22wI/Bu61YF8BH+gnnEW7eVU9IskhAFX1i4E2WN+qqq76jKuqrybZo6q+NaDD+XD3GLLrVNXn1/g3v7KvYDbS/muMvDoryUlVtX+SR/cW1Wwm0wlfvGBfsfp1a7n7TZLN6e7Jk6yg9ewuOZPc2e1ASxAnw1OuSxuK9tskv+ovrOlV1ZBvGic+291cXTvJvWm9VB/qOabFWJFkp6q6ANrcBWD77rVBDNWsqsHNW12H9yR5M3CDbjj84xhOb/pClyb5G1ovG8AjgMu6D51ePmhmVVV/D62hoap+1nc8G+FnSbZj1Yf9vqz67BiS33Vz79/XbT90wWuDaFysqsf2HcMm8usk12bVOXVzWs/u0JzTdRIsvE59o2uI+01/YU2vqo7u/i92qqpz+o5nkX7UnUOT8+mhtOktQ7RVkn2q6jSAbh7oVt1rg0jcq+qefcewCbwW+CBwwyQvo31evKCPQJyTO6Mkj6f9Zx1P6wG9G23O1buAF1XVc/qLbjpdq++jgF2q6iVJbgLcqKo+33NoU0uyGfB44D60/4ePA/8+tPlWSe4HvAn4Ju04dqEl7MfT5k8u+yHw3RyxZ9I+6A8b6hwxgK7B5Kpzqqo+2XNIM0uyPXAkcJdu14m0VuHLaf9H5/UV27SS7Ae8BdiqqnZKcnvgiVX1lJ5Dm0k3zOx1tPmGZwMrgId2QwIHI8nNgNfQhsgWcCrwDOB7wJ2q6sQew5vKWOYedteoFwC3oY0i2x/4i6o6vs+4ZtUlh0+hXadCu069gTbE8TpV9X89hjeVJA8EXglsWVW7JLkD8OKqelDPoU2t+9s+itaDeBnwbeDRVXV+n3EtRpK9aNMnJontFcATgK8A96+q9/QV27RGVMfhVsABtL/tT1XV13qJY2A5wbKQ5EbA3rT/vM93c/cGYzlNChd0Lde3op1PXx9CsamFkhwDrAQe0908Xhs4paru0HNoV2tJthrCjeLaJDmN1vp7XFXt0e07u6p27zey2XVTKW5J+/s+p6oG0Us1Nkk+S5tX/OYRnFPbAfvSzqlTq+pHPYe0KEPvBU2ykjaM9PgF59RZVfVH/UY2u256zmZVdUXfsWysJNen5Tc/6TuWWSX5KG3e/fOr6vbd58cZQzqn1lGT5Yo+Pvscrjyjrhf0AOBm3XyknZLsPaReUNqk8DsmOQPapPAkvUwKX6yMoIoerFqIF1QAACAASURBVNYLetNJpcwkQ+sFHcUcsYyggixA2hJh/05rzR5sL2hVfXeN02hwc1nXUthotySXA2dV1cV9xLQYI+kFHcXcwwVFaCZDSnfqbuq/M6kVMgRJHgT8M60gzSB7QYErq+ryNc6pQfUcZY2Cft2xXA6srKov9RLUIo2kF3QMdRy+yFpWeEiy5Cs8WF15dm+gDdk6pNu+Anh9f+EsyrKZFL4RxlBFD1qL3a8ZdqXMscwRG0MFWYBXA39CK7JDVX2ZNq1iSL7bJeuVZMskzwZ6Ge60kR5Pa3CYXKf+jdaodVKSP+8zsBmNoQLrWOYevoE2XPwo2v/LKbRr1DeS3KfPwGZ0JG1E3E8AuoRq5z4DWoSzk/wZsHnXQP064OS+g5rRnsCTaJ97O9CWZrwH8G9JnttjXIvxdoZfzX4MdRyWzQoPJrmzWzalsTfCmpPCT6S1fg3J/lX1vKo6q3s8H7hHVb2CYX1Q3ryq/olVN4+TatdD8iLaRe0mSd4JfAoY2ocjtAqyb66qK6rqp1V1FO1CfQywTd/BzaKqvrvGrqG1BD+JVq17B1rDzx1YvXr3UPwOuHVVHVxVB9PmUf4K2Af4m14jm8111jJaaTC9hp2nAm8GbpXke7Sb3yet/1uWpfMZ/tI70PWC9h3ERvpr4La0v+l30ZKRoSVV2wF3rKpnVdWzaEnvClrD6F/0GdgibN/Nu/0dtF5QhvfZ9yzgOODmSU4C3kE7z4Zkz6r6+GSj2prXd6uqU4ElXeHB4cqzG3wvaFW9s5tLMpkU/uC+JoVvhMFX0esMvhe02hI1K1k1R+zwgc4RG3wF2c5qvaDA0xheL+j/VdWj+g5iE9i5qn64YPtiYLequjTJkObmDr4XtJtu8McjmHs4hqV3YI1eUNp1ami9oH/YNbA/v+9ANsJOrL6Sw29o06d+kYGsGLLA4HtBq2plkrsz7DoOy2aFB5Pc2S2b0tiLleTFwOeAt9dwl+d4AvDWJFvRLgQ/BZ7Q3cD8Y6+RzeZFrN4Luj8Daz1N8h+0oeKfq6qv9x3PRngUrYLsG1hVQfbRXSPEX/UZ2IyeRDuOSS/oJxheL+jZSX5Iu06dAJw00F6fzyX5H+C93fbBwAnddWpIRVGeShseO+kF/Tbt72UwknyT9jc9Oae+2m9Eizb4pXc6f01LDie9oB8HXtJrRLN7e5IdgC+w6jPwrJ5jmtV/AacmObbbfiDwru4aNbS/kTV7QVewemP1spdkcn36HO1zb0h/0xN/RpuO8N+sqpz+Z8DmwMOXMhCrK0+pK4rw5aqq5VIae7GSPI5Wtn8/2pzizwEnVNWx6/3GZWioVfSSPBg4uaouHnqlzCT3op1PdwVuBnyJdj69ptfArma6m5QTab0hX6iqQayzvC5pa0bfldbwcz/gJ0Or2N0VYDuYdgyTD/v310A+eJP8K3AS7Vr1vSH3gnZJ4D6sOqduRftMf0ivgc0oA196J8mXWXWdOmmIS9Us1I2W2Ys2j/WJtGXP1lZddtlKsicLrlFVdXrPIc0kydNp16kzul2D7QVNW9Jpcj+1L60R6HNV9YxeAxsok9wpJTmdtobpF+k+9GkJyU97DWwjJPlDWqvKs4Ftqup6PYc0kyT3p82HudZkX1W9uL+IppfkfbRGhp+z6nw6aeEwtCHphqHsBdyT1pP4i6q6Vb9RzSbJtWiFgtY8px7XW1AzSPIA2lqHdwb+CDiHVefWyWsMm13WkuxI+5C/O3B74FLazdeQRmkMXpK/YtU5Bd11qvv65aoazFSdtKU49qKdU3ehzUU8s6qe2GtgVzNJdmfVOXVn4Lp01yjadeq0HsObSZJJMnJXWgXZL9ESknf1GtgiJLkhq3/uXdBjODNJ8krauXQr4ExWXadOqapL+4xtMdKWKb077by6J3BBVR3Yb1TT66ZxPpffv5e615LHYpI7vbTlXvZm1cV5L1o135OGtDxHkn+nFUCZDAc8EfjiwJYfeBNwHdoF4N9pQ1I+X1WP7zWwGSXZmVXn0360+TFfqKr79RjWTJJ8inajcgrd+TSk5VEmkrwX+DptWM2LacMxv1ZVh/ca2CJ0jQ570HoXngTsUlWb9xrUDJL8jjYE8B+GOMJkopsT9jrg1rQChZsDP6uqrXsNbBG6G6/9adeqg4AVQzqOJD8HzqJVUf/foSwzt6Zu/uo/0j7DF95A3qy3oDZCku1plbqfzvCuU7+lLTH3j8BHhjh6plvK6V9oFYkvpt2DfL2qbttrYIvQ9arvyar7qf1oI4Bu02tgM+imVfyINoz8c8CXhtSYCJDkE8AxtA60JwGHApdU1ZIXW3RO7gyq6ufA8Um+AJxG+8B/DDCYFpbOdrSbrZ/Qekh+NKQEt3PnqrpdkjOr6u+T/Avwgb6DmlVVnd/1IF67e0yeD8mZwJ2A3WlFHn6S5JSuUvSQ3KKqHpbkoKo6Osl/0eaJDUZ3wzhpNNmXdj79L60BYkj2oPW2/VmSI4Bzgc8ObL1DgP9Hu4F/L+3m6zHALXqNaEbdkOs/op1T+9OSq3NpVT+H5BDaOfUUWv2Gk2nTKj7Vb1gzexttvturaY28j2VAFfkXNMBNzqebA9+jNVYP7Tq1He0Y7gY8rWucO6WqXthvWDN5Ce2z4n+rao8k92TVEplDc21ga+D63eMiWsPWkLyWdp06hPZ38tkkJ1TVN/sNaybbVdVbkhxeVZ+lHcNn+wjEntwpdVUA70xbyuJXtF6G02gXtB/0GdtiJbk1bT3NZwCbV9WOPYc0tSSnVdU+SU4F/pS2JujZVbVrz6FNJcnf0loZV9CGlZ7aPc6sqqGVvAegKwL2WFrr3R9W1ZKWit9YST5fVXsnOYF2I/wD2uiAQfSQJDmX1sjwftq59IXlPj9vfbrzaTIc8NFAVdXOvQY1oySnV9WeXWPc7bp9J1fVnTf0vctBkk/Sbhq/RHeNGloNijV1NTXuS+s5vGFVDapRMcnKqrpTkrOq6o+6fZ+rqrv2Hds0kvyMVu399cDxVfXtnkPaKN191GRo6Z1pQ0vv3m9U01twjfoybWmq300+C/uObVpJjqINjb2Cdl8+uVZd1mtgG2GN+6kdBzbC4dSq2jfJx2lJ+0XA+6rq5ksdiz250zuKNpTxTbTW32/0HM+idXP37kprfdwG+DRtWMSQ/E+SGwD/TJsnXbSW4KF4DPB/wP/Q5o+cNtDqsZN5e3el9eZ+B3grwzufAI5Ksg2tWvpxtCWphtQi/1Zai/zBtJ633ZOcApwxtIaTrgbCNWl/GyfS1tj7Tr9RLcrPuyF0X0ryT7Rld67bc0yz+BZtTvSutIbEHyW5ZGjF8QCSvJ/WSH0e7Zx6DO2GeGh+mWQz4Nzu2vs94IY9xzSLJ9AaeJ8APLYbGXcKrcPge71GNqNuaOk5tPPpTcBjBzhk+SddQnUC8M4kFzOsZRihDbG+Jm2EyfdoqwoMqhjpRDcq8S60+49Tgb9jePdTL+2Kwj6LNl1na3paP9qe3Cl1Q2xuz6qhgLek3bBMLs6f7jG8mSR5PavK3V/UdzyLkeSaVfWryXPasMxfTvYNQZJtWX1o6VbAl2nFN97WZ2yzSPIc2vm0coDD3q+SZJc1exXWtm8IkuzGqnlJd6XNhxlS78KKqrqk7zg2VpKb0mofbEkbMXN94PUDG3pGkq1p16jJtWoFbeTMob0GNoMke9FqTwyqwWdN3XF8jVbo6CW0G8h/GlLBpokFdU4mS+dtWVU37TWoGSTZbGjzJdfUVUz/BbAZrQ7F9YH/HFrBpm5axW1ZdU+1O2063ilVdWSfsc0iycNoHWmDKRS5piT7V9VJG9q3JLGY5C5Okj+gFTt6BgMrlgBXxb9Xt/n5oRUKSvLFqrrjhvYNQVf18060nvUnMszz6fa0ZApa48mX+4xnMdZxTq2sqjv1FdNipC1BMJnvdmdaQZHTquoBvQY2g64V+Eja3wTAZ4EXD220Qzcn6TUb2rfcdQ2Je7HqnNoXuHgyXHYIklwDeDKrn1NvGuASIw+rqvduaN9y1iVW+7DqOrUX8F1aEc/BrEmeVgX+dbRjKFqP7uFVdWGvgc0gySvWLAi0tn1D0f2fTK5TD6DND71Bv1HNpisGdtV1qqo+1Gc8s1pO9+cmuVNKcjtWL3u/Ja0Xd7L0y2DWFetail4JHE8rWHFX4DlV9b4+45pG2rJHOwD/SauCOym4sTXthmUQy9Z0F7HJB/xtga+w+jIKg+nFSvI04DBWFf56CHBUVb2uv6im183Ruy3wT8BzFry0Ne3vYhBVJpN8kJZ8XE67Nk3WN/1qr4EtQje09Gzg6G7XnwO3r6o/7S+q2a3jw/6Mqtqjr5hmkeTVtOvUrrR5uQuvUYMaDpi2qsA1WP2c+m1VPaG/qGa3nG4gFyPJGXSrCLDqOnXqEOsHdHPW/wv4j27Xo4FHVdW9+4tqNus4n66qITAE3T3I5H7qN3TLB3VfzxpSb3uSf6SNbnhnt+sQ4PSqel5/UU0nyX60/4en0wrjTWwNPKSqbr/kMZnkTifJwvVxTx7o/DAAugID95703qatafW/fZyAs0pyKG1Y05600v0TVwBvr6pBVFhO8gFWreW2coDzeK6S5Exgv6r6Wbd9XdoQoUF8SCY5CHgw8CDaXNyJK4B3V9XJvQQ2o67h5OQNzZdMcu+q+uQShbUoSb5UVXfY0L7lKskhtEa4u7D6fKrr0RKrP+4lsBl1N48ns4F53UluW8t8je8kX17zM25t+5arJPcF7kdb2/6YBS9tDdxmKIWCug6Ds2oDN59JDq2qo9f3nr4N+TqV5Mm0Aos3AxZOn7gerePm0b0EtghJXsWqDqfvr+d92yz3YlTd/dQdJol5N1XyjCHcTyW5O6uWLXzTgpeuAD5UVecudUwWnprStK2kSd5fVQfPO56NtNkaw5N/TJuPsex1H3pHJzm4qt7fdzyLNW2PVNpSPPvNO56NFGDhDfBvYThLWlRbh/XYJPtV1dCWsLhKVR234XcB8ApgWSe5wC+S3KWqToQ2n4c2b2woTqbVbNietgblxBW0JbcGoapeO+Vb/wNY7j2Jv01y88l86G5Y/5Dm514ErKQ1xq1csP8K2rSpQaiqac//w1nV675c/SjJo4F3dduH0O6nhuC/gI/S1vg9YsH+K4Y2H7eqnjnlWz/F8r9OQZtvP/k/uH6fgcyiVi0X9Pbl0hFokrvpDWG5kY91pb0nF+ZHAB/pMZ6pJXnm2p5PVNWrljaiubtW3wFM4W3Aad1w2QAHAYNZzzTJ62jzqSY9cKupqqcteVDzNYQGiCfTGrOuT4v3UtqC8oPQfcB/h1b46+pgCOfUc4DPJPkWLd6b0pboGISuzsGXk/znkAv8zWAI59TjaGthT4ZmntTtG4LNgZ8CT13zhSTbDi3RndIQzql/BM5I8hlavHcDlv1QZYAkH2LVvdTvvV5VD1rqmExyN71lP/67qp6T5E9pQ+lCmz/5wZ7Dmtb1+g5giQ3hfHpVkuNp5xO0ZRTO6DGkWQ1mPv0mMoRz6kvA7buqvlTVT3sOaSZJrmDt/86hrfe79RKHNG9DOKc+lWRX2soIAb5ew6rGfxbrv4Fc9sMZZzSEc+oCWs/6EK1k1b/xmidUMYwOm1kN4Zx6V3c/NSkM+zdV9YMeQ5rFK/sOYE0muVdfJ9OGav2OVgBiEKrq7/uOQesU2vk0hNbSq6w57yvJ9dru4RVCGYsk29GqK98FqCQn0qorD2IoYFVd3Rrjlr0k16LNQbwL7Wb3c0neVFW/7DeyqQ2mOvomsuw/R7oh76+hFfwrWrGjZ1TVt3oNbApVtUvfMWid9mPVdWpzYBCdUN1wZQDS1offrds8p68q9oOYhzkwQ7gwPwH4PK0K7kOBU5MMZYgN0MrEJ/lgkouT/DDJ+7vS8WMzhPPp72hzp7ahzUF8W5IX9BvV7JLs3lX+PBv4apKVSQZRWXlG5/cdwBTeDVwCHEy7Rl3C6sV2BiPJ7ZP8VfcYW2/bxBAK572DVkX9dbQhprdhVVXcZa+qvjN5AL8E/qh7/GK5zH/bxJZ8Tc1F+C/gPcCNaEu1vZdV08AGI8mDkryye4y5MWUI91NvoBVuOot2L/LEJK/vN6rZJLkHcC7weuANwDeS3G293zSvWKyuPJuuNfgWtBaWb67ZCpzkPlX1iV6Cm1KSc4A7T3pFul6Tk6vqlv1GNr0xlO5flyQnVdX+3fPdq+rsvmNanyRfA/aY/C0kuTbwxaq6db+RzSbJycDzq+oz3fY9gH+oqjv3GthGSnJv4LlD+tvIWtYnTnJ6Ve3ZV0yLkeRw4C8Z7vJaaxZpKeBHVfXdPuLZGEOvrjyR5OHAPzPAJQAnuoqx20wqwXe9Pn9B6wUdzOdGktOqap819p1aVfv2FdOskrycNjR2cEvWTCPJBVW1U/d82c81TvIVYPdJ9fEkm9GqkQ+mwT3JSuDPquqcbns34F1rfqYvBYcrTynJFsA/0IoKfIfWC75jkrfRbox/A7DcE9zOhbSKjBNX0BZiH5IVVfW2BdtvT/L03qLZtHaaPFnuCW7nfFqBrEmDzzVZfUmCobjuJMEFqKrju+WQBiHJvWhl+28M/DftevUO2o3wy3oMbTE+k+SRtF4SaL25H+4xnsV6PLDPguW1XkEb0jiIJJfVK0NPbNslJYd0c6eH4owk+1bVqQBJ9mEYvYVrej6w15pLAAKDSHK7v+s3Az9Lci7wIlpj9ReAR/UY2mJ8JskRtJEnRSvi+eEk2wIs94Sqcz9WX7LmaOAMBlLsaApX9d4O5P/jHNo94GR0xk0YUEX+zjUmCS5AVX0jyTX6CMQkd3r/TCt6tEtVXQHQFUV5Zfc4vMfYZvU9WjXcY2kX5oOAz0+qFQ+kQvGQS/dvyNCGV/wK+ErXu17AvYETk7wWBlWd+FtJXsjqowO+3WM8s/oX4DBaEnVf4FTghVX1ml6jWpwnAs9k1f/F5rSb4mcyrMJNQ19e655r259kT+C1tMqfQ7EP8JgkF3TbOwFfmxR0GlDhpsEuAdh5AXCnqjqvGylwCvDIARW/XOgR3dcnrrH/cQyreNMgl6yZ0tDup7ajXZc+323vBZyS5Djop0LxIpye5C2s+vx+FKsve7ZkTHKn9wBgt4ULmFfVT9MW1P46w0pyv8nqPW3Hdl+HVCxlYen+ohXSGsy84q669VpfAq69lLFsAh9k9cIIx/cUx8Z6HPD3tKGlAT7LgJYYod2oH989/+8klww0wd1g4aYkt62qryxVPBth4fJaAA9mQMtrrUtVnZ5kq77jmNGB63sxyTZVddlSBbMRBrsEYOfXVXUeQFV9Mcm3B5rgbrB4U5J7V9VyX5N8sEvWTKxtOcnJS8DQrlN/13cAm8CTaUtTPY32f3ACbW7uknNO7pSSfKOqdpv1tSFK8rqq+uu+41ifJCuq6pK+41isbpj7OlXVkJKr9Ury/qo6uO84NiTJHgNb+mg13fqfz16w65ULt6vqA7/3TQOV5ItVteZ80WWp662aLNd2wpDPsYkkfwB8pI85VvMysHNq4RKAJwwpSUxyIbBwtNgzF24PZCTZVIZyTiW5Ea3HMMBpA1qyBoAkR67v9TGtypHklKpa1uuvJ3kI7fOh9yXa7Mmd3leTPKaq3rFwZzdk9us9xTQv+/cdwBROTvJtWsXV91fVT/oOaBZjSmKnMJQhW6/qPuzfC7x7ID2FC30WeOA6totVxY/GYBBDfpO8Bjimql7bdyyLkeR1/P5wv22BOzOs0UvTGMo59QzgvQNutPo3Vh81tub2mCz7c6obBvsu4LhJ7YChGVMSO4Vr9R3AFB4E/GuSE2jz1T9eVVf2EYg9uVNKsgPtJvEXrFpEey/a0NKHVNX3egxvkxpQ6+PewCNpQwC/SktM/rPfqKaznuE1gK3ZfUnyh8DDaUMAt6YlKC/tNyqtaSjnVJJDaefSbrQh/cdU1en9RjW9Lv6FijYH9AtrzAsdvAGdU0fSrlGX0m4g31dVP+w3Kq3NEM6pJHenXaPuT1ta8hjgfwa0fjST+h/rMqC6IBs0hHMKoCs0dV/auXUX4JNV9YQlj8MkdzZdBdPb0lrovlJVn0pycFW9v+fQNpmh/BFNJNmeNtzpUVW1ed/xTGON4TVPpFWbvMqYWiaHdj4BJPkj4LnAI6pqy77jmYYNJ8tXV231YFqj3E5VtWvPIW20JPtX1RCrE6/VAM+p29FuIA8GLqyqP+45pKmYkCxP3bJO96IteXbggIr7rdkY9/fAasOXq+ropY1ofgZ2Tl2DVgvhscBdq2rFUsfgcOUZVdWngU+vsfvVwGiSXIYxxGZr2pqTjwRuTusl2bvXoGawMIlN8uAxJbVrsezPJ4Akt6bdND6U1lv1buBZvQY1m4VD/n6v4WRkft13ADO6BXArYGfaqJNB6G58Hw7sAHysqs5O8gDgb2mjmPboM75NbBDXqQUuBn5Au1bdsOdYZrGwyurvJSQjc37fAUwjbW37B9I+/+4IDCopXJjEJnn6mJLatVj216kkB9Luze9JK0T677TPkaWPxZ7cjZfku1V1k77jmEVXGbPWNgcjyV9U1duXPqrpdfNx/xt4T1Wd0nc8G2NILXPTSnJMVT2ie36fIawfneRU2tyk91bVRX3HszGSnFFVo0lAktwSeHZV/WXfscyiWxf3T2nV7I8BPjik+gFJ3k5bp/HztCV4vgPsBxxRVf/dY2hTm6xZukABP6k1bn6SbDuEdTS7FR0eAaygrY17TFUNpuFkoRFep+4NPLeq7t13LNNKcgztb/tjtHXJj5+smTtEI72fOqmq9u+e715VZ/cd0/okeTetk+CjfRefsid30xhMS0GSpwBHANdtm7kCeEVVXVXee7knuJ2brXmTstAQKkSP3FXV/4aQ4AJU1b7/v727j7WsvOo4/l1QIyjFYk000pTpMG1ACLS1TSXSF9uowQKKGqINJRGtUZvYkmBoYm1i1cYGfGmlEUsDSY3+04hFajQatDAToWnFqQpS2+LUl74opg0ax2jr8o9nn8y555577t539tnPWs/+fRKSmbsn4Rn47XvPevaz19p0PUuX6E6a70nLuiOYtwPfTNnE+g3K6IGXUeYAZ/MPwBXu/tS6iwlGIb0EuMzd/8/MzgKeAo4k67666KGx/ATkHDP7OPBj7n4CIEOB27kAeLO7H193MdEoJMj7ferVwJ2c+j71DuD9lIz9UsWlHcQ9wOvc/SvrLiYZg9S65y5+Eb3ABXD3H9p0fcoO0Spye1oMjF93CfjGiZdzIGb2VkpXzFe5+5Pd1w4D7+p2sdM02NlU4HZCd4heydMRM/vrxSXKX++yOiuTDbJ0ic7sLuA3gYcp7/I8Cvwu5X37NI1QFtz9zn3+yG9TjgdG9T+Lpzru/t/duLxMBe6es0y7MTx3ss/83Gjc/S37/JEHiJ2pFvwK8OOU71NXAY8AP+cJ55K7+x/v80feCYQucruHNYvPU19jZk8vLlE+T6V5v3gPKTeDNpisQ7SK3P6urr2AEbweuHz5w6K7P2lm1wMfB9IUuQ1In6du/ufaS8BXTbmWiYT+QdPIxslXL50k+YSZ3UI5Grv2KUMDor9fddFKji7sfp8pU2u5+73dxm9rQmeqkYLE3f3D3a8/aGb/lrHA7Sl0ngDcvdcIqsinHLpNt7WXKP0PWjLZZykVuT25+2dWv9Z19f33Hk8Vw1j3NMTdT5pZ2ncwMmokT5uOj7Y2OzqD9BsnwFlm9iJOfbD6T+AyMzMAd3+02sq2I/q9fnHtBWxL15fijNrr2ILQmWqhIAGetVKU2PLvPe8M43VC52mgyKccrtlw7UOTraIxKnJ7MrNvA36ZMpvuFyjHzL4BOMPMbuxx5COCfzaz17j7A8tf7N4v+VylNW1L6N3HFvLk7t9Rew0TC52pRjZOPk8ZB7bu904ZcSETaSFTe4zWOg+4Frhj4uVIf5ELkgfZWZQs/96BlorcloT9Ge7uP1J7DROa7P+Ditz+7qCMTfg6ygihq9z9ETO7iNKRNXxRAvw0cJ+ZHeNUM46XUt5f/d6aCzuITR2igehHh9LnacPxGqCN3ezlLtHArVUXs49GNk5eVXsNEws9CqmFTLFztBaUn3ufB25w97+psJ5tC/tBfqCwf4+ZFSQnai9gRGE35lqfc7/cIZry6uQ0/94km7HVmdlxd39h9+u/c/eLl66laYPfdch8HXAJ5YfIY8DvUN7V/UjNtfW12iEa2NUhOroW8mRm9yz99hrg/qXfu7vfNPGSRmdm/+juz93/T9ZnZh/j1MbJe1nZOEmSqaY3TrKNQmohU5uY2QXrnlZH1NoopP1EHgXTckGScQxSX8EztTwvetece3f/+WlXNK5ao1b1JLe/5XdWT65cS7NT0L2Te/fq183sAyy1KY+qoQ7R6fO0vJvdFeZz2t2O6BmLcU1m9nZ3fwTA3Z/oXmnN4JqVX+/YOCHJMcCGRiG1kCnM7ArgfOAhd//X7v/PW4CXU+YAZ9DaKKTMlk8H7CpIMmhsDFJfYb9pLRexZvZ92YvaNap8rlWR29/lXRdAA85e6Qg4WTvsLQp7869opUN0a3lKUZiv01CXaG2cxNHKKKT0mTKz2yhN2Y4Dt5rZh4CfonyoT3PapLVRSD2E/UzSSEHSzBikBTN7O3AU+Is9XmN7zcRLOqgU31tXRewQrSK3J3c/s8+fC94RcJM0N1ULHaJnkKdMWukSrY2TOFoZhdRCpl4LvKib83se8FngMnf/ZOV1jSLrKCQVJNW1OAbpBPDDwLu7UVVHKac37gOdcphAuA7RKnLHF7YjoJndz/pvyAY8e+LlHNScOkRDnjwdNrM/WL7u7tdOv6rhWukSrY2TUJoYhdRIpk4uNkbd/Ytm9olWClxIPQrpBCpIampuDJK73w3cbWbfBFwP3EJ5Wt1rbFVNLcy5j3jySo2nRha5aZCZvXLTdXd/cKq1uLMsbAAACelJREFUHJSZXQLcB6ztEO3uj1Vc3uiUp+1rvdnRquDNN5Y3Tl4BPLR8PcvGiZl9mL2f8Li7NzUKKXimvsTOHO3IVaJMbRyF5O53TbykUawUJOd5zzm6Na0WJMCnFpdIUpCsNI5clbJxpJm9D/gW4AuUTZNjwKPu/uWqC+vBzC7YdD1Dg7yIDdn0JHd8YXcNshQdm7j7Y2Z2KTs7RD9Eaf5wec21bYnytH1NNDsaIOy7bpRmTQuZGjTtMMNRSJEztToeL2uumhqFtKYg+UHKu+sZXF17Aacr4lO3ETwbOBP4EmXs2VMZClzoX8Sa2cPufsW213NA4Rqy6UnuyILvaD+fMg7ii8CvUpqjvBz4NKU740crLu+0ZRr30lfiPP2ou3+s4vIOJPKT87FEzlQrdDpAppRpFNKCmf0+pbPv48CDlKPKT9Zd1bgiFyQRn7qNxcwuBr4buBk4092fU3lJo8nyGSXKOvUkd3yRd7TvobSIPxf4CPBm4DpKYXIHZbxFZpH/2x9U5L/Tpjy9h5x50q5fRQ1tnMztdEBYDWWqlVFIALj7dbCjIPlzM2uqICF2c7ZwT91Ol5ldTbkXXkE5yv9nlFMCLcnyGSXEOvUkd6D9OgJGHsRuZsfd/YXdrz/l7kfWXcsq45Nc5SmWOTyRirLDuo6ZHePUxsnNlI2T+ykfXH7R3dNtnET+7z2WyH/HVjK1MgrpCKVb6WIU0m8lG021riB5GDjaNQ9qQpafJ5Hv3yHM7D2U19eOuvtna69nGxJlKsQ69SR3uBPk7Qi4PGLn6Q3XwmqkQ/SyEyhPVbXSJXoh+WiOc9z9vQBm9hPu/oHu63/afcjPKP1OsjIVQmujkK6iFCTvarUgSST99ygAd3/jpuuRj48PEPZ0X8QO0SpyB8rcohy4qAudAReuBPBwvWUNcvsBr4WkPIXQRLOjJSfQxomM6wTKVG1NjUJSQSIVRD4+DoCZ3UR5Er3Xvf36KdczULiGbDquPJBalMuYlCfZlqSjOf6LMo7DgAvZOZrjsLt/ba21DdHKKKRVylQ9rYxC6ivDEdr9ChIzu9Td/3biZfXSwhikoaIcod2kOzVzJXABZUzmUUrGjldd2Iim3MDSk9zh1KK8ogY7RCtPlbXUmAbSj+a4uPYCRtLU6QBlKoRWRiH1leEJzCHghm7Dd1dBErXA7YR76ibg7m8DMLOzgTcAPwP8OuVzYisme6KuIncgdQSsrqkO0cpTCK11idbGSWUNzZBeUKYqazBT6WUuSFq5LwYKf3zczN4KfDtwDvBXlFMz6hB9QCpyB1KL8upaaSICKE9BNJUpbZzU19rpAGWqvtYy1YMKkhhC3xcDRX6fdeH7gS8Df0iZH/1Its7pkajIHU4dAetqpYnIgvJUX1OZ0sZJCE2dDlCmQmgqUz2oIIkh+n1B1wxvr6kb7u7nQvjj4wC4+4vN7JmU93K/E7jLzL7g7ldWXtqYJtvAUuOpkbVwtCNyw4dWmoj0pTxtX2uZ0qzA+lqbIa1M1ddKpvoWJFksFSRXUpqyNVWQRL8vWmNml1I2rl4JvAT4J8r33bdVXdgAkRqy6Unu+MIf7UjeoryVJiJ9KU/b11SmNJojhKZOByhTITSRqQwdufvaqyCpuqjxRb8vWvNOyqmAdwMfdff/rbyegzhEkIZsepI7sgy7XmpRnofyFEdDmQr9ZB1i7QQfRGunA/ajTG3f3DKVgZktjikfI2lBkv2+mBsz+z13/4Ha6+hjqSHbLcD57j55QzY9yZ2hzB0BBwj/BLQVM8kTtJOpDDubhwiyE3xATZ0O6EGZ2r65ZSo8d3/tputJCpJD5L4v5uZw7QXsJ1JDNhW54wt/tCNSALcow4euPpSnOFrJVHjZN05mOp4jNGVKKghfkGS/L2Yow+eQMA3ZVOSOL/r7hxAogLIv5UnGpo2TOFo5HaBMxdFKploQviCZ0X0hE4nUIVpFbk9qUZ5O6A9dylNKoTM1gDZO4gj/IbgnZSqOVjIl05jLfZGCmZ0FHKHcx59e8/8i/OeQSA3ZVOT2pI6AsWTv6Ks8xZM9U9o4kbEpUyJ7a6Eg0X0Rg5k9A3gHcBPwGeAM4Dlmdg/ws0tNzW6ttMQhwnSIVpE7T2ECeBoOoWYJUbSQJ0ieKW2cpBT6Q7AylVLoTLWgpYJkRvdFdLcBzwSe5+7/AWBm5wK3d/+8CcDd/6TaCnuK1JBNI4RklyQdAYEYLcpls0x5AmUqghZGc4DGc0SiTMlYzOzXKAXJzWsKkpPu/qaa6xuilfsiOzP7JPACXynKzOxM4Al3f36dlY1vypFzKnJllyQzD1ebJRyj/OD/XNWFyS4Z8gTKVCZZNk7mMkO6BcqU9DWzgiTFfZGdmf29u79g6LWMzOxRd3/xFP8uHVeWdTLsfKhZQh4Z8gTKVCbhR3OAxnMko0xJX75a4HZf/IqZZfl511eK+6IBj5vZje7+/uUvmtkNwBOV1pSeilxJSc0SZGzKVCopPkhqPEcqypT0NaeCJMV90YA3Avd2ryP8JeW/+0uBs4Hrai5sqEgN2VTkzlCkAB6UmiXE0UKeQJmSrdDpABmbMlVfMwWJxODu/wK8zMxeDVxC+dz0R+7+QN2V9RexIZveyZ2RvQII7AigmX1X9A5uapZQX0t5AmUqkv02TrK85w2wdDrgSuB6QKcDKlCmZGwrBcljmQqShZbuC6krYkM2FbkzEjGA26JmCds3pzyBMjWFBjdO1p4OWLxXKdunTIns1tp9IfVFbMimIndGIgZwW7T7uH1zyhMoU1NobeNEpwPqU6ZEdmvtvpD6InaIVpE7IxEDuC1TtiifqznlCZSpKcxw40SnA7ZMmRLZbW73hWyfmX0QuHePhmzXu/u1U69JjafmZU4dAWX7lCcZ25xGc4DGc0xBmRLZbW73hWxfuIZsKnLnJVwAD6qVjr7JNZMnUKaCmNvGiT5Mbp8yJbLb3O4L2bKIHaJ1XHmGMncEVLOEeDLnCZSpSMzsfOBe4CRrNk66H6LN0BH47VOmRHab230h86QiV1JRswQZmzIVT/aNkwWN54hDmRLZrZX7QmQdFbmSipolyNiUKRmbTgfI2JQpEZFhzqi9AJGB9myWgN5FkoNRpmRstwFfDzzP3b+1e7J2IfAsygkBAFSMyADKlIjIACpyJZvHzezG1S+qWYKcBmVKxnY18IbF8XcAd38a+Enge6qtSjJTpkREBlB3ZcmmqY6+EoIyJWPTeA4ZmzIlIjKAilxJJWKLcslNmZIt0HgOGZsyJSIygBpPiYiIjEjjOWRsypSIyDAqckVERLZA4zlkbMqUiEg/KnJFRERERESkGequLCIiIiIiIs1QkSsiIiIiIiLNUJErIiIiIiIizVCRKyIiIiIiIs34f2DDEWUIPjqLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SENSOR FEATURES VARIANCE ###\n",
    "\n",
    "df.loc[:,features].std().plot.bar(figsize=(16,6))\n",
    "plt.ylabel('standard deviation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_9ItJqxGFhHt"
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTION TO GENERATE SEQUENCE FOR LSTM ###\n",
    "\n",
    "def gen_sequence(df, seq_length):\n",
    "    \n",
    "    seq_df = []\n",
    "    \n",
    "    for start, stop in zip(range(0, len(df)-seq_length), range(seq_length, len(df))):\n",
    "        seq_df.append(df[start:stop, :])\n",
    "        \n",
    "    return np.asarray(seq_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### RETRIVE CYCLE NUMBER ###\n",
    "\n",
    "k = -1\n",
    "cycle = []\n",
    "for i,time in df.Timestamp.iteritems(): \n",
    "    if time==0.:\n",
    "        k += 1\n",
    "        cycle.append(k)\n",
    "    else:\n",
    "        cycle.append(k)\n",
    "        \n",
    "df['Timestamp'] = cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16445, 8, 18) (1148, 8, 18)\n",
      "(16445,) (1148,)\n"
     ]
    }
   ],
   "source": [
    "### CREATE AND STANDARDIZE WINDOW SEQUENCES FOR EVERY CYCLE ###\n",
    "\n",
    "sequence_length = 8\n",
    "test_size = 0.1\n",
    "\n",
    "X_train, X_test = [], []\n",
    "Y_train, Y_test = [], []\n",
    "\n",
    "for k,group_df in df.groupby('Timestamp'):\n",
    "    \n",
    "    if int(len(group_df)*test_size) > sequence_length: # at least the dimension of the test\n",
    "    \n",
    "        init = group_df[features].values[0] + 1e-3\n",
    "        y = group_df.Labels.values\n",
    "        x_train = group_df[features].values[:int(len(group_df)*(1-test_size))] / init\n",
    "        x_test = group_df[features].values[int(len(group_df)*(1-test_size)):] / init\n",
    "\n",
    "        scaler = StandardScaler()\n",
    "        x_train = scaler.fit_transform(x_train)\n",
    "        x_test = scaler.transform(x_test)\n",
    "\n",
    "        x_train = gen_sequence(x_train, sequence_length)\n",
    "        x_test = gen_sequence(x_test, sequence_length)\n",
    "        y_train = y[sequence_length:int(len(group_df)*(1-test_size))]\n",
    "        y_test = y[int(len(group_df)*(1-test_size))+sequence_length:]\n",
    "\n",
    "        Y_train.append(y_train); Y_test.append(y_test)\n",
    "        X_train.append(x_train); X_test.append(x_test)\n",
    "    \n",
    "    else:\n",
    "        continue\n",
    "    \n",
    "X_train, X_test = np.vstack(X_train), np.vstack(X_test)\n",
    "print(X_train.shape, X_test.shape)\n",
    "Y_train, Y_test = np.concatenate(Y_train), np.concatenate(Y_test)\n",
    "print(Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "cztafwq5s0Ny",
    "outputId": "40f695a2-7b18-4801-cc27-f229bb2dee35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    710\n",
       "1    438\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### LABEL DISTRIBUTION OF TEST SET ###\n",
    "\n",
    "pd.value_counts(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "FVFzlKjmop6b",
    "outputId": "0a4550bf-1015-4b0f-f920-93b13a339c68"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT EXEMPLE OF SAMPLE ###\n",
    "\n",
    "_id_ = 39\n",
    "\n",
    "plt.plot(X_train[_id_])\n",
    "np.set_printoptions(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CO1UsEMytDnv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49335, 8, 18) (49335, 8, 18) (49335,)\n"
     ]
    }
   ],
   "source": [
    "### GENERATE PAIR SAMPLES FOR SIAMESE NETWORK ###\n",
    "\n",
    "os.environ['PYTHONHASHSEED'] = str(33)\n",
    "np.random.seed(33)\n",
    "random.seed(33)\n",
    "\n",
    "left_input = []\n",
    "right_input = []\n",
    "targets = []\n",
    "pairs = 3\n",
    "\n",
    "for i in range(len(Y_train)):\n",
    "    for _ in range(pairs):\n",
    "        compare_to = i\n",
    "        while compare_to == i:\n",
    "            compare_to = random.randint(0,len(Y_train)-1)\n",
    "        left_input.append(X_train[i])\n",
    "        right_input.append(X_train[compare_to])\n",
    "        if Y_train[i] == Y_train[compare_to]: # They are the same\n",
    "            targets.append(1.)\n",
    "        else:# Not the same\n",
    "            targets.append(0.)\n",
    "            \n",
    "left_input = np.asarray(left_input).reshape(-1, sequence_length, len(features))\n",
    "right_input = np.asarray(right_input).reshape(-1, sequence_length, len(features))\n",
    "targets = np.asarray(targets)\n",
    "print(left_input.shape, right_input.shape, targets.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "_lnE-Fs5tDqW",
    "outputId": "faabeb71-8206-4892-9fca-55c6b2f70e50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    32570\n",
       "0.0    16765\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### LABEL DISTRIBUTION OF AUGMENTED DATASET ###\n",
    "\n",
    "pd.value_counts(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kSK-Y5fVtDlA"
   },
   "outputs": [],
   "source": [
    "### DEFINE SIAMESE NETWORK ARCHITECTURE ###\n",
    "\n",
    "def set_seed(seed):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    \n",
    "\n",
    "def SiamesNet():\n",
    "    \n",
    "    set_seed(33)\n",
    "    \n",
    "    left_input = Input((sequence_length, len(features)))\n",
    "    right_input = Input((sequence_length, len(features)))\n",
    "\n",
    "    lstmnet = Sequential([\n",
    "        BatchNormalization(),\n",
    "        LSTM(128, activation='relu', return_sequences=True, \n",
    "             input_shape=(sequence_length, len(features))),\n",
    "        LSTM(32, activation='relu')\n",
    "    ])\n",
    "\n",
    "    encoded_l = lstmnet(left_input)\n",
    "    encoded_r = lstmnet(right_input)\n",
    "\n",
    "    L1_layer = Lambda(lambda tensor: K.abs(tensor[0] - tensor[1]))\n",
    "\n",
    "    L1_distance = L1_layer([encoded_l, encoded_r])\n",
    "    drop = Dropout(0.2)(L1_distance)\n",
    "    prediction = Dense(1,activation='sigmoid')(drop)\n",
    "    model = Model(inputs=[left_input,right_input],outputs=prediction)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "AzC8v8Z-tDtK",
    "outputId": "db0ba06e-4afb-43f7-d655-bba9c794ece2",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "82/82 - 8s - loss: 0.6425 - accuracy: 0.6541 - val_loss: 0.6287 - val_accuracy: 0.6753\n",
      "Epoch 2/50\n",
      "82/82 - 7s - loss: 0.6244 - accuracy: 0.6673 - val_loss: 0.6108 - val_accuracy: 0.6882\n",
      "Epoch 3/50\n",
      "82/82 - 9s - loss: 0.6104 - accuracy: 0.6848 - val_loss: 0.6042 - val_accuracy: 0.6954\n",
      "Epoch 4/50\n",
      "82/82 - 10s - loss: 0.5984 - accuracy: 0.6972 - val_loss: 0.6007 - val_accuracy: 0.6994\n",
      "Epoch 5/50\n",
      "82/82 - 9s - loss: 0.5889 - accuracy: 0.7080 - val_loss: 0.5924 - val_accuracy: 0.7150\n",
      "Epoch 6/50\n",
      "82/82 - 8s - loss: 0.5778 - accuracy: 0.7165 - val_loss: 0.5857 - val_accuracy: 0.7164\n",
      "Epoch 7/50\n",
      "82/82 - 8s - loss: 0.5654 - accuracy: 0.7260 - val_loss: 0.5746 - val_accuracy: 0.7199\n",
      "Epoch 8/50\n",
      "82/82 - 8s - loss: 0.5545 - accuracy: 0.7317 - val_loss: 0.5708 - val_accuracy: 0.7267\n",
      "Epoch 9/50\n",
      "82/82 - 7s - loss: 0.5408 - accuracy: 0.7454 - val_loss: 0.5683 - val_accuracy: 0.7241\n",
      "Epoch 10/50\n",
      "82/82 - 7s - loss: 0.5302 - accuracy: 0.7541 - val_loss: 0.5530 - val_accuracy: 0.7357\n",
      "Epoch 11/50\n",
      "82/82 - 9s - loss: 0.5146 - accuracy: 0.7661 - val_loss: 0.5431 - val_accuracy: 0.7444\n",
      "Epoch 12/50\n",
      "82/82 - 8s - loss: 0.5010 - accuracy: 0.7742 - val_loss: 0.5362 - val_accuracy: 0.7506\n",
      "Epoch 13/50\n",
      "82/82 - 8s - loss: 0.4898 - accuracy: 0.7804 - val_loss: 0.5427 - val_accuracy: 0.7385\n",
      "Epoch 14/50\n",
      "82/82 - 8s - loss: 0.4743 - accuracy: 0.7915 - val_loss: 0.5243 - val_accuracy: 0.7517\n",
      "Epoch 15/50\n",
      "82/82 - 9s - loss: 0.4563 - accuracy: 0.8051 - val_loss: 0.5119 - val_accuracy: 0.7687\n",
      "Epoch 16/50\n",
      "82/82 - 8s - loss: 0.4455 - accuracy: 0.8094 - val_loss: 0.4832 - val_accuracy: 0.7915\n",
      "Epoch 17/50\n",
      "82/82 - 9s - loss: 0.4360 - accuracy: 0.8171 - val_loss: 0.4811 - val_accuracy: 0.8000\n",
      "Epoch 18/50\n",
      "82/82 - 8s - loss: 0.4205 - accuracy: 0.8257 - val_loss: 0.4642 - val_accuracy: 0.8025\n",
      "Epoch 19/50\n",
      "82/82 - 7s - loss: 0.4223 - accuracy: 0.8248 - val_loss: 0.4611 - val_accuracy: 0.8049\n",
      "Epoch 20/50\n",
      "82/82 - 7s - loss: 0.4102 - accuracy: 0.8315 - val_loss: 0.4958 - val_accuracy: 0.7818\n",
      "Epoch 21/50\n",
      "82/82 - 7s - loss: 0.3981 - accuracy: 0.8364 - val_loss: 0.4578 - val_accuracy: 0.8085\n",
      "Epoch 22/50\n",
      "82/82 - 9s - loss: 0.3866 - accuracy: 0.8445 - val_loss: 0.4349 - val_accuracy: 0.8208\n",
      "Epoch 23/50\n",
      "82/82 - 9s - loss: 0.3824 - accuracy: 0.8437 - val_loss: 0.4296 - val_accuracy: 0.8188\n",
      "Epoch 24/50\n",
      "82/82 - 7s - loss: 0.3740 - accuracy: 0.8516 - val_loss: 0.4228 - val_accuracy: 0.8288\n",
      "Epoch 25/50\n",
      "82/82 - 8s - loss: 0.3619 - accuracy: 0.8535 - val_loss: 0.4187 - val_accuracy: 0.8272\n",
      "Epoch 26/50\n",
      "82/82 - 7s - loss: 0.3564 - accuracy: 0.8588 - val_loss: 0.4157 - val_accuracy: 0.8285\n",
      "Epoch 27/50\n",
      "82/82 - 6s - loss: 0.3418 - accuracy: 0.8646 - val_loss: 0.4383 - val_accuracy: 0.8218\n",
      "Epoch 28/50\n",
      "82/82 - 7s - loss: 0.3352 - accuracy: 0.8693 - val_loss: 0.3917 - val_accuracy: 0.8453\n",
      "Epoch 29/50\n",
      "82/82 - 6s - loss: 0.3197 - accuracy: 0.8759 - val_loss: 0.3985 - val_accuracy: 0.8393\n",
      "Epoch 30/50\n",
      "82/82 - 7s - loss: 0.3158 - accuracy: 0.8767 - val_loss: 0.3724 - val_accuracy: 0.8508\n",
      "Epoch 31/50\n",
      "82/82 - 6s - loss: 0.3150 - accuracy: 0.8774 - val_loss: 0.3638 - val_accuracy: 0.8553\n",
      "Epoch 32/50\n",
      "82/82 - 6s - loss: 0.3104 - accuracy: 0.8801 - val_loss: 0.3890 - val_accuracy: 0.8429\n",
      "Epoch 33/50\n",
      "82/82 - 6s - loss: 0.2977 - accuracy: 0.8855 - val_loss: 0.3559 - val_accuracy: 0.8627\n",
      "Epoch 34/50\n",
      "82/82 - 6s - loss: 0.2898 - accuracy: 0.8886 - val_loss: 0.3538 - val_accuracy: 0.8592\n",
      "Epoch 35/50\n",
      "82/82 - 6s - loss: 0.2782 - accuracy: 0.8942 - val_loss: 0.3641 - val_accuracy: 0.8568\n",
      "Epoch 36/50\n",
      "82/82 - 7s - loss: 0.2823 - accuracy: 0.8913 - val_loss: 0.3928 - val_accuracy: 0.8357\n",
      "Epoch 37/50\n",
      "82/82 - 7s - loss: 0.2648 - accuracy: 0.8987 - val_loss: 0.3677 - val_accuracy: 0.8572\n",
      "Epoch 38/50\n",
      "82/82 - 6s - loss: 0.2660 - accuracy: 0.8992 - val_loss: 0.3179 - val_accuracy: 0.8785\n",
      "Epoch 39/50\n",
      "82/82 - 7s - loss: 0.2491 - accuracy: 0.9067 - val_loss: 0.3145 - val_accuracy: 0.8829\n",
      "Epoch 40/50\n",
      "82/82 - 6s - loss: 0.2450 - accuracy: 0.9078 - val_loss: 0.3499 - val_accuracy: 0.8637\n",
      "Epoch 41/50\n",
      "82/82 - 7s - loss: 0.2479 - accuracy: 0.9057 - val_loss: 0.3405 - val_accuracy: 0.8730\n",
      "Epoch 42/50\n",
      "82/82 - 8s - loss: 0.2335 - accuracy: 0.9126 - val_loss: 0.2983 - val_accuracy: 0.8914\n",
      "Epoch 43/50\n",
      "82/82 - 8s - loss: 0.2262 - accuracy: 0.9151 - val_loss: 0.3068 - val_accuracy: 0.8842\n",
      "Epoch 44/50\n",
      "82/82 - 6s - loss: 0.2191 - accuracy: 0.9180 - val_loss: 0.2878 - val_accuracy: 0.8876\n",
      "Epoch 45/50\n",
      "82/82 - 7s - loss: 0.2272 - accuracy: 0.9136 - val_loss: 0.2968 - val_accuracy: 0.8927\n",
      "Epoch 46/50\n",
      "82/82 - 7s - loss: 0.2062 - accuracy: 0.9245 - val_loss: 0.2861 - val_accuracy: 0.8927\n",
      "Epoch 47/50\n",
      "82/82 - 8s - loss: 0.2068 - accuracy: 0.9229 - val_loss: 0.2729 - val_accuracy: 0.9047\n",
      "Epoch 48/50\n",
      "82/82 - 7s - loss: 0.1864 - accuracy: 0.9306 - val_loss: 0.2762 - val_accuracy: 0.8980\n",
      "Epoch 49/50\n",
      "82/82 - 6s - loss: 0.1887 - accuracy: 0.9293 - val_loss: 0.3083 - val_accuracy: 0.8868\n",
      "Epoch 50/50\n",
      "82/82 - 7s - loss: 0.1818 - accuracy: 0.9322 - val_loss: 0.2640 - val_accuracy: 0.9031\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x267cdb3c908>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siamese_net = SiamesNet()\n",
    "siamese_net.compile(loss=\"binary_crossentropy\", optimizer=Adam(lr=1e-3), metrics=['accuracy'])\n",
    "\n",
    "siamese_net.fit([left_input, right_input], targets, batch_size=512, epochs=50, \n",
    "                validation_split=0.15, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "I_TRZ-FXtDvg",
    "outputId": "4bd5bf3f-a44f-4da4-9e7e-7e75f2b506f2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1148/1148 [08:07<00:00,  2.35it/s]\n"
     ]
    }
   ],
   "source": [
    "### COMPUTE PREDICTION ###\n",
    "\n",
    "os.environ['PYTHONHASHSEED'] = str(33)\n",
    "np.random.seed(33)\n",
    "random.seed(33)\n",
    "\n",
    "real = []\n",
    "pred = []\n",
    "rep = 4\n",
    "\n",
    "for j,i in enumerate(tqdm.tqdm(Y_test)):\n",
    "    \n",
    "    test = X_test[j]\n",
    "\n",
    "    nofault = np.random.choice(np.where(Y_train == 0)[0], rep)\n",
    "    nofault = X_train[nofault]\n",
    "    nofault_sim = np.max([float(siamese_net.predict([test[np.newaxis,:,:],s[np.newaxis,:,:]])) for s in nofault])\n",
    "\n",
    "    fault = np.random.choice(np.where(Y_train == 1)[0], rep)\n",
    "    fault = X_train[fault]\n",
    "    fault_sim = np.max([float(siamese_net.predict([test[np.newaxis,:,:],s[np.newaxis,:,:]])) for s in fault])\n",
    "    \n",
    "    pred.append('nofault' if nofault_sim > fault_sim else 'fault')\n",
    "    real.append('nofault' if i == 0 else 'fault')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177
    },
    "colab_type": "code",
    "id": "Rw4Md74gaYbw",
    "outputId": "1fc131c6-37c7-47f3-efdc-3115894cc815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       fault       0.69      0.16      0.25       438\n",
      "     nofault       0.65      0.96      0.77       710\n",
      "\n",
      "    accuracy                           0.65      1148\n",
      "   macro avg       0.67      0.56      0.51      1148\n",
      "weighted avg       0.67      0.65      0.57      1148\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(real, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "FFDsxzX_oqCD",
    "outputId": "fce207c3-9358-4283-eb18-4cdf4ee0f71d"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=25)\n",
    "    #plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90, fontsize=15)\n",
    "    plt.yticks(tick_marks, classes, fontsize=15)\n",
    "\n",
    "    fmt = '.2f'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\", fontsize = 14)\n",
    "\n",
    "    plt.ylabel('True label', fontsize=20)\n",
    "    plt.xlabel('Predicted label', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fsS2PPHR/h5f77xuuY/gGG3PsTw5ng9VX4vObrsPvfnUC06dPn9eSpXnWq+dCrLPKQG597LVm4295/DU2Gja45jz3PTeWZfotyvbrDQVgwBILs9tmq3DTI682a3fsN9dnzLh3ufT2/3VN8aqpoYI3In4REa9HxKyIuKgTlzsyIsZXDK9ajuvbWa+huZs4YTwzZ85k4NLNv2wGDhrE2+PGdni5r44ZxQ3/uIbp06dz/uXX8IMf/4LLLjqPX59w9LyWLM2zgUv0oWePhRg76cNm48dN+rDFY7YPPD+OvX57Gxf+YCumXLkfr138bSJg/9PumN1mm7WXZ9dNP8H/ndVyHwl1jYYJ3ohYDzgWOB3YFDi+C19uVeAYwOCth2g+mFIiImq3bYNZs2YxYODSnHTqmay19rp8ccev8oMfH82lF51LSmkei5U6SdU/xSBo6Z/nsBX6csr+m3DylY+yyZHXsuOx/2Jw30U4/TubA8UW8LmHbsn+p/2HSe9P6+LCVa1nvQvoRMPK5zNSSlPqWom6RL/+A+nRowfjq7Zu33n7bQYuPaiFueZu0OBl6NmrFz169Jg9bpVPfZoPP/iACe+MZ8DApTu8bGlejX93KjNmzmJwv+Zbt0v37cO4yR/WnOeHu3yWh//3Nqf+7QkAnhozgQ8+ms6tv/wyIy99iJWXWZJl+y/GDcduP3uehcofr+9etR/rHnoV/3tjchetkRpii7fcrXxJOTg5IlJEfCkiTo+I5yPig4gYFRFnRMSSFfOtVLbdoXp5EfFwC681AriuHBxVzj+601dKc+jduzdrrr0Od//ntmbj7/7Pray7/kYdXu7wDTZmzKiXmDVr1uxxo156kUUWXZT+AwZ2eLlSZ5g+YxaPvjSerddevtn4bdZegfufq32IZdGFezKz4t8zwMxZxeZxBPz3xbcZfthVbHj4NbMf1z80hnuefYsND7+G0ePsaNWVGmWL93jgVeDnwNbAh8CbwA7Az4C3gSHl31cC83Ky2iPAkcBvgJ3L1/loHpandtjvO4dyxPf2Y+111mP4hhtz2UXnMu6tN/nm3vsDcMT39gPglDPOnz3PM08+DsB7705hoYUW4pknH6dX79586tOrAfDNfQ7gkvPP4rifHsG39z+Y114Zw+9/dTzf2ufAedqFLXWW0/7xJOcfNoKH//c29z03lgO2W41l+y3KeTc9C8B5h44APj6Ge/1DYzjzu1twwHarcfNjr7Fsv0X59b4b88hLb/Pq+PcBeOaVic1eY9L70+jRY6E5xqvzNUTwppReioiXysGHUkrvlX8f3NQmInoCo4C7I2JoSqn2iZ9zf60pEfF8OfhoSml0R+tW++3w1d2YOHECp596Mm+PfYtVh63BBZf/jeWHrAjAG6+9Ouc8WzffGr71putZfshQ7nqk+BiXW34If7ryOk78xY/40lYbsvSgwey2x1587/Afd/0KSW1w1T0v03+JhfnxbuuwTL9FefqVCex0wo288nbxVTdk6ebn8/759v+xxCK9+c72a3DyPhsx+f1p3PnUG/zs4gfrUb6qRKN0HomIvYELgSWagjci9gQOBz4FVP7L/FxK6ZaIWIkijHdMKf2zYlkXAWumlNYrh0cCh6SUBpbDO1Dsbl65teCNiAOBAwGWW2HI8LsffaET1lTqPlY/4JK5N5Ia0NS/Hfjfpgyp1hDHeGuJiK8CFwP3AbsBGwFfLSf3yVFDSumclNJ6KaX1+g+wg44kqUF2NbdgN+CBlNJ3m0ZExJZVbaaWz72rxte+xJEkSfOoYbd4gUWYs9PTN6uGxwHTgdWaRkTE4sDGc1l204lvWbacJUmNo5G3eG8GzoiInwEPANsDza41mFKaFRF/B34QEWOAScARFL2iW9PUueqgiPgL8EFK6clOrV6S1JAaeYv3bOAU4DDgGmBFYI8a7Q4B7gHOBM4ALgduq9FutpTSGIpTinYu572utfaSJDVpmF7N87u1Pjs8/eOWe+pdhpSVvZq1oFogezVLkjQ/MnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjAxeSZIyMnglScqoZ0sTIuLlDi4zpZRW6eC8kiQ1tBaDl2JrOHVgmdHBWiRJangtBm9KaaWMdUiStEDwGK8kSRl1OHgjol9EDOnMYiRJanTtCt6IWDwiTomIt4DxwKiKaRtGxA0RsW5nFylJUqNoc/BGxFLAfcAPgDeAZ2nekepJYHPgG51ZoCRJjaQ9W7w/A9YA9k4prQtcWTkxpfQB8B9gm84rT5KkxtKe4N0ZuCmldHErbcYAy89bSZIkNa72BO8KwBNzafMesFTHy5EkqbG1J3jfBQbNpc3KFJ2uJElSDe0J3oeAHSJiiVoTI2JZYHvg7s4oTJKkRtSe4P09MAC4ISJWq5xQDl8J9AFO67zyJElqLK1dq7mZlNJNETESGAk8BUwHiIjxQD+KU4t+lFK6t/PLlCSpMbTrAhoppeMoThf6BzARmElxI4UbgG1TSr/u9AolSWogbd7ibZJSuh24vQtqkSSp4XmTBEmSMmr3Fm9ErATsCaxDcc7uZOBR4M8ppVEtzylJktoVvBFxBHAi0Ivm12neCfh5RPwkpfTbTqxPkqSG0ubgjYhvAL+m6FR1GnAH8BawDLAVcCjw64h4PaX0184vVZKk7q89W7xHUITuuimlMRXjnwf+ExF/Av4LHAkYvJIk1dCezlWrA1dUhe5s5fHdKyjuYCRJkmpo77WaJ82lzSRgSsfLkSSpsbUneP8NbNfSxIgI4PNlO0mSVEN7gvcooF9EXB4RK1ZOiIihwGVA37KdJEmqocXOVRFxW43Rk4CvAbtExCvAWGAwMBToQXG/3kspLispSZKqtNarecRc5vtE+ai0NsW1myVJUg0tBm9KyctJSpLUyQxXSZIyMnglScqo3TdJAIiIFYDlgYVrTU8p3TkvRUmS1Kjae5OEzwOnAsPm0rRHhyuSJKmBtXlXc0RsCPyT4lzd0ynuTnQncC7wXDl8HXBc55cpSVJjaM8x3p8CU4H1U0qHleNuTyl9B1gTOB7YFriqc0uUJKlxtCd4Nwb+kVJ6o3r+VDgGeBY4thPrkySpobQneJcCXqkYngYsVtXmHmCLeS1KkqRG1Z7gHQf0qxpepapNL2CReS1KkqRG1Z7gfYHmQXs/8LmIWBUgIpYBdgH+13nlSZLUWNoTvDcCW0ZE/3L49xRbt49GxEMUPZuXBn7XuSVKktQ42hO8Z1Mcv50OkFK6B9gNGEXRq/lN4OCU0sWdXaQkSY2izRfQSClNAR6oGnctcG1nFyVJUqPyWs2SJGVk8EqSlFGLu5oj4uUOLjOllKpPM5IkSbR+jHchIHVgmdHBWiRJangtBm9KaaWMdUiStEDo0P141X69ewTL9u1T7zKkvEY/Xu8KpPmOnaskScrI4JUkKSODV5KkjAxeSZIyMnglScrI4JUkKSODV5KkjNp9Hm9EfAbYA1gNWCyltG05fiVgA+DmlNLETqxRkqSG0a7gjYjjgJ/y8ZZy5SUlFwIuB74P/KFTqpMkqcG0eVdzROwO/By4GfgscFLl9JTSy8DDwJc7s0BJkhpJe47xHgq8CHwlpfQEMK1Gm2eBT3VGYZIkNaL2BO9awE0ppVqB2+QNYPC8lSRJUuNqT/AGMGsubQYDUztejiRJja09wfs/YJOWJkZED2Az4Ol5LUqSpEbVnuC9Alg3Io5oYfpPgE8Cl81zVZIkNaj2nE70O2A34FcR8TXKU4ki4jfA5sB6wP3AOZ1dpCRJjaLNwZtS+jAitgJ+D3wT6FFOOpzi2O+fgUNSSjM6vUpJkhpEuy6gkVKaDOwdEYcD6wMDgMnAgymlt7ugPkmSGkq7LxkJkFKaANzUybVIktTwvEmCJEkZtXmLNyIuaGPTlFLar4P1SJLU0Nqzq3nvuUxPFBfZSIDBK0lSDe0J3pVbGN+XoqPV0cC9wI/ntShJkhpVe04nGtPCpDHA4xFxE/AEcAtwfifUJklSw+m0zlUppVeB64DDOmuZkiQ1ms7u1TwWbwsoSVKLOi14y5skbE1xQQ1JklRDe04n2qKVZQwB9gE+C5zXCXVJktSQ2tOr+Q7KGyO0IIA7gR/OS0GSJDWy9gTvcdQO3lnARIrrNT/YKVVJktSg2nM60cgurEOSpAVCmztXRcQFEfGDrixGkqRG155ezXsAg7qqEEmSFgTtCd7RGLySJM2T9gTvZcAXI6JfVxUjSVKja0/wngQ8DNweETtExOAuqkmSpIbVaq/miPg28FhK6QlgatNo4O/l9FqzpZRSe05TkiRpgTG3gLwIOIbirkN30foFNCRJ0ly0Zcs0AFJKI7q2FEmSGl9n351IkiS1wuCVJCmjtuxq7hsRQ9uz0JTSKx2sR5KkhtaW4D2sfLRVauNyJUla4LQlIKcAk7q6EEmSFgRtCd5TU0rHdXklkiQtAOxcJUlSRgavJEkZGbySJGVk8EqSlFGrnatSSgazJEmdyGCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ4JUnKyOCVJCkjg1eSpIwMXkmSMjJ41e2c/cczGfaplem7eB822WA4d999V6vt77rzP2yywXD6Lt6H1Vb9BOeefVaLbX918i9ZpFfw/UMP6eyypXly4G6b8+w/RzLx/lO559Kj2HSdVVptf9DXtuDRq3/OhPt+y+PXHs0eO2wwR5slFuvDKUftysv/PpFJD5zKU38/hl0+t05XrYJKPetdgNQeV17xV448/DB+/4cz2WTTzTj7rDPZaYcv8sgTzzB06NA52o8eNYqddtyeb++9Lxf86c/ce8/dHPZ/32Xg0kvz1Z13adb2gfvv54Lzz2WttT6Ta3WkNtn18+vymx/uymEn/ZV7H3uJg762BX87/busu8sJvPrWxDnaH7DbZpxw2Ff43vGX8eCTo1l/zZU44+hvMGnKB9xw51MA9Oy5EP8883tMfPcDvnXU+bw2dhIrDO7LR9Nm5F69BY5bvOpWTvvdb9nz23uz7/4HMGy11Tj1939gmWWX5dyz/1iz/bnnnMWyyy3Hqb//A8NWW4199z+Ab+25F7/77W+atZs8eTL77PVNzjrnfPr265djVaQ2O/RbW3PJdfdz4bX38vyosRz+/67krfGTOWC3zWu23+NLG3DhNfdwxY3/ZfTr73DlTf/lgmvu4Yi9Pze7zbe/vDFL91+C3b5/Dvc+9jKvvDmBex97mf8+80qu1VpgGbzqNqZNm8ajj/yXbT73+Wbjt93289x/370153ng/vvYdtuq9p/fjkf++zDTp0+fPe57Bx/IV3felRFbbd35hUvzoFfPHqyz2hBuve+5ZuNvue85Nlp75Zrz9O7Vk6lVW64fTp3OemuuSM+exdf+jlt9hvsee5nf/mg3Rt38Sx65+mf87KDtZ09X12mYdzgiFoqIMyJibESkiBjZycu/KCIerhjeoLNfQ60bP348M2fOZPDgwc3GDxo8mLFj36o5z9ixbzGoqv3gwYOZMWMG48ePB+CC887l5Rdf5Jhjj++awqV5MLDf4vTs2YOxE6Y0Gz9uwhQGD1iy5jy33Pcs3/7KRgxfvTj8su7qQ9n7q5vQu1dPBvZdHICVlx/Azp9bh149e7DzoX/kuDOvZ/9dN+P4//tK166QGuoY787Ad4H9gGeA17r49TYAjgFGdvHrqFpEs8GUElE1rnnzOds3jX/h+d8shvsAABVsSURBVOc55uifcsvtd9G7d+/Or1XqLKn5YBCz/y1XO+ncGxk8YEluv+gIImDchHe59LoHOGKfzzFz5iwAFlpoId6e8C7fPf4yZs1KPPrsq/RfajF+deTO/OTUa7t6bRZojRS8w4CJKaUL6l2IusbAgQPp0aMHY99qvnX79rhxDBo0uOY8gwcvM0f7cePG0bNnTwYMGMDNN93I+PHjGf7ZNWdPnzlzJnffdSfnnXMW70x+n4UXXrjzV0Zqo/ET32PGjJlzbN0u3X8Jxk14t+Y8Uz+azneOvZRDTrycwf2X5M3xk9lvl02Z8t6HjJ/0PgBvjZ/M9BkzmTXr4/B+ftRbLLbIwgzstzjjJ77XdSu1gKv7ruamXbgR8bmIeCIi3o+IuyNijYo2i0bEaRHxVkRMjYiHIuLzFdPvAI4H+pW7mVNErBQRy0bEBRHxckR8GBEvRMQJEdG7Yt4RZfs1q+q6IyKuaqHmvYE/lH83vd4dnfm+aE69e/dmnXWHc9stNzcbf+utN7PRxpvUnGfDjTbmtttuaTbutltuZt3h69GrVy92/MpOPPzokzzw8GOzH+sOX4/dvr47Dzz8mFvBqrvpM2by6LOvsvVGw5qN32ajYdz/+KhW550xYxavj5vErFmJ3bYbzr/uenr2VvJ9j73MKkOWbrZH6JMrDuL9Dz8ydLvY/LLFOxT4NXAi8CHwG+CKiFgzFf9KzgW+DPwUeBE4ALg+IrZKKd1NsYv5cGBX4AvlMt8EVgUmlNMmlsMjgaWBg+ah3uuBU4AjgI3LcVNabq7Ocuj3D2e/vfdkvfU3YONNNuXcc87izTfeYP8DvwPAfnt/G4DzL7oYgAMO/A5nnXk6Rx7+ffY/4CDuu/ceLrn4Iv7058sB6Nu3L3379m32Gostthj9+vVnjTWb/RaT6ua0P9/G+Sd8m4efHs19j73MAbtuxrJLL8V5VxXnsJ93/J4A7H/0JQB8cugg1l9rRR58cjT9lliUQ/fcmtVXWW72dIBzr7yL73x9C045alf++Jf/sOJyAzj6O9tzzhWtnxeveTe/BG9/YNOU0v+g6CgFXAt8OoqfY98A9kkp/amcfhPwBHA0sF1K6ZmIeA2YkVK6v2K5TwJHNg1ExD3A+8AFEfF/KaVpHSk2pfR2RIwu/75/Ls3ViXb72teZ8M47nHzSCbz15pusscaa/O26G1hxxRUBePXV5qdCrLTyyvztuhs46ogfcO7Zf2TZ5ZbjlFNPm+McXml+dtW/H6H/Uovx4/2/wDIDl+TpF99kp/87k1feLM7hHbJM/2bte/QIDv3W1qy64mCmz5jJnQ+/wFZ7n8Irb06Y3ea1sZPY8btn8P+O2JkH/vJjxr4zhT/9/X5OPvfGrOu2IIqWDs5nKyDiImCzlNInK8atCjwPfA5YDvgTsFhK6YOKNscAR6WUFiuHRwKHpJQGVrQJ4DDgQGBloE/FS38qpfRiRIwAbgfWSik9VTHvHcD4lNKuFXWumVJarxw+BPhDSqnFXj0RcWD52gwZOnT4Cy+Nac9bI3V7/db3CmBaME197Iz/NuVFtbof4y1Nqhpu2hLtAywLvFcZuqWxwKIR0VrPl+9T7BK+FvgKRU/k71Usu0ullM5JKa2XUlpv6YFLd/XLSZK6gfllV3Nr3gQWj4hFq8J3MPBBSumjVubdDbgypfSzphERsXpVm6nlc3Uvmv7A+A7WLElSTfPLFm9rHqI4g23XphHlLuRdgbvnMu8iQHUwf7NquOl839Uqlj8E+PRclj2tbNvlW86SpMYx32/xppSejYjLgdMjYkk+7tU8DDh4LrPfDBwaEQ8AL1GE7icrG6SUXouIh4DjI+IDih8jP6XoDd2apuu3HRYRtwFTUkrPt2PVJEkLoO6wxQtF0P6Johfz34EVgR3KU4lacxxwOXBC+TwNOLRGuz2AV4A/A78s55tbiN5FcQrUYcADwNltWRFJ0oKt7r2aFxTDh6+X7nng4bk3lBqIvZq1oOoOvZolSVogGLySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRpFSqncNC4SIeBsYU+86FlADgfH1LkKqA//t18+KKaWla00weNXwIuLhlNJ69a5Dys1/+/MndzVLkpSRwStJUkYGrxYE59S7AKlO/Lc/H/IYryRJGbnFK0lSRgavJEkZGbySJGVk8KqhRMTLEbF2C9PWjIiXc9ckSZUMXjWalYCFW5i2KLBCvlKkvCLitogY1sK0VSPittw1aU49612ANK8iYkmgb8WoZSJiaFWzPsDuwOvZCpPyGwEs2cK0JYEt8pWilhi8agQ/AI4BUvm4toV2ARyRqyipTuY4RzQiegNbA2/lL0fVDF41gsuAhymC9R/AkcDzVW2mAc+nlF7JXJvUpSLiGOAX5WAC7o+Ilpr/OktRapUX0FBDiYgtgUdSSu/WuxYph4hYH9iA4ofnacApwOiqZtOA51JKd+WtTrUYvJLUICJiL+D6lJK3ApyPGbzq9sp7Hbf5H3JKaVAXliNJrfIYrxrBGbQjeKVGEhEP0b4fnht0YTlqA4NX3V5KaWS9a5Dq6Gn84dmtuKtZkqSM3OJVQ2nLbjd3tUmqJ4NXjabWbrf+wMbAh8Ct2SuSMomIK+bWJqX0tRy1qGUGrxpKSmnvWuMjYnGKi2vcm7UgKa+la4zrD3waeIc5LyyjOvAYrxYYEfEl4PSU0sr1rkXKKSKGUFxK9diU0nX1rmdB592JtCDpC/SrdxFSbimlV4GTgF/Vuxa5q1kNJiK2rzG6N7Aaxc0Ubs9bkTTfmIm3xZwvGLxqNP+k6FxVfZX46cDfgUOyVyRlEhGr1xjd9MPzeOChvBWpFo/xqqFExIo1Rk8FxiX/savBRcQsap9OFxSh+42U0st5q1I1g1eSGkR5d65qU4HXUkqv565HtRm8akgRsQKwKtCnelpK6Yb8FUlSwWO8aigRsQRwBfD5plHlc+UvzB5Zi5Iyi4iewFBq//B8Jn9FqmTwqtGcRPGFszlwN/BVYCLwLWBr4Bv1K03qWhHRCzgN2AtYuIVm/vCsM8/jVaPZHjgReKAcfiOldGdK6UCKXs0/rFtlUtf7BbADsB/F3p5DgH0oLpU6GtixbpVpNoNXjWYw8GpKaSbwPsXl8prcwMe7oKVG9DVgJMXhFoAHU0oXp5Q+T7EH6Cv1KkwfM3jVaF4FBpZ//4/i13+TDSl6eEqNagjwQvnDcyrNr9R2KbBLXapSMwavGs3NwLbl36cC34uIeyPidooLCFxct8qkrvcmxaVRAUYBW1RMWyV/OarFzlXq9iLiAuD4lNIoit3JTwOklC6JiPeAXYFFKI53nV23QqWudwdFx8LrgHOB30TEJ4GPgK8Dl9evNDXxPF51exExE9g4pfRg5d/1rkvKLSKWAQamlJ4qh3/Axz88bwaOSym9X8cShVu8agxvAiMi4hmKnpx9ImLRlhqnlD7IVpnUxSLiF8B5KaU3KK7LPPueuymlUykOuWg+4havur3yi2ckta9RO4eUkucxqmGUe3k2Sik95B6f7sEtXnV7KaXjIuJ6ijuwXAycALxU36qkbN4GVqe4CULQxh+gqh+3eNVQIuJCiuNYo+pdi5RDRJwBHAxMoDh9aDIwo6X2KaVBmUpTCwxeSerGIiKAnSn2+BwHnA+81lL7lNKxmUpTCwxeSWoQ5fnqB6eUnqt3LWqZwStJDSoieqWUpte7DjXnlaskqYFExCYR8a+IeBeYGhHvRsQNEbFxvWtTwS1eSWoQEfE54HqKc3mvBMZS3DhkV+DTwJdSSrfUr0KBwStJDSMiHgReAXZLVV/uEXE1MCSltEFditNs7mqWpMaxFnBudeiWzimnq84MXklqHJNo+S5Enyynq84MXklqHFcCJ0XEtyKiD0BE9ImIbwEnAlfUtToBHuOVpIYREYsA5wG7l6PeAxYv/74c2D+lNLUeteljBq8kNZiIGAasDyxLcfeuh7yoxvzD4JWkBhMRqwIrAH2qp6WUbshfkSp5dyJJahARsTrwV4q7FUWNJgnwtph1ZvBKUuM4G+hNcdOEZ4Bp9S1HtbirWZIaRES8B+yeUvpnvWtRyzydSJIax0vUOK6r+YvBK0mN4wjgpxHxiXoXopa5q1mSGkREPAQMBfoBo6lxpSqv1Vx/dq6SpMbxVPnQfMwtXkmSMvIYryRJGRm8kiRlZPBKDSoiUkTcUTVuZDl+RH2qap/21hsRF5XtV5rH170jIrr0OFxn1arux+CV5kH5xVn5mBkR4yPitoj4Zr3r6wq1Al1S29mrWeocx5bPvYBPAzsBW0XE8JTS4fUraw6nA38BXql3IdKCyuCVOkFKaWTlcERsA9wMfD8iTkspja5HXdVSSuOB8fWuQ1qQuatZ6gIppVuB5yjuELM+ND9eGRF7RMQDEfFeRIxumi8iFo2In0TEYxHxfjn9voj4Rq3XiYjeEXF0RLwUER9FxKiIOCEiFm6hfYvHTCNiWERcEBGjy2WNi4i7IuLgcvreFcc9t6zaxT6yalkbRsRVEfFWREyLiFcj4uyIWK6FuoZHxI0R8W5ETImIWyJi47m8zW1W1n51RLwcER+Wr3FPRHxrLvMtXL6fo8r35KWIOCYierfQflh57PbVsv3YiLgsIj7dWeui7s8tXqnrNN2WrbqTzhHA54DrgNuBpQAioi9wG7AO8AhwAcWP4+2AyyJijZTSz2cvPCKAK4CvUFyj93SKO9PsC6zVrkIjvgRcCSwM3AhcDvQF1gaOAv4IPEaxS/0YYAxwUcUi7qhY1j7AucBHwD+AV4FPAfsDO0bERimlVyrabwLcUtZ+DfAi8Nlymbe1Zz1a8UeKu/XcSXFj+AHA9sAlEfHplNLRLcx3BcUPp6uA6RTv9UhgvYj4cqq4EEJEfKGsvxfFZ/sixT1xdwa+FBFbpZQe6aT1UXeWUvLhw0cHHxShmmqM3xaYVT5WLMeNLNu/D6xTY56LyulHVY3vQxGGs4DPVozfo2x/H9CnYnx/iiBOwB1Vy2qqYUTFuIHAZIpbyG1Zo64VaqzzHdXtymmrlst5EVi+atrWwEzg2opxQbFnIAFfqWp/WNP7W1nvXD6Ppvdwparxq9Ro2xu4lSJQq2u9o1zOC0C/qs/ivnLanhXj+wETKXbjr161rDWA94BH2lKrj8Z/uKtZ6gTlLtyREXFiRFxFEZQB/C6lNKaq+TkppUer5h8AfAt4OKX0q8ppKaWpwI/K5e1RMWmf8vmnZZum9hOA49tR/l7AksAfU0r/qZ6YUnqtHcs6mGKL77CU0utVy7mNYgt4x4hYohy9CUVntDtTSn+vWtbpFD8g5llKaY7lpJSmAWdQ7PnbpoVZj08pTayYZyrwk3Jw34p236bYQ3BMSumZqtd5mmIPwDrljeq1gHNXs9Q5jimfE8WF6e8Czk8p/blG2wdrjFsf6AHMcby01Kt8Xq1i3LoUW8F312h/x9xLnm2j8vlf7ZinJU3HZbeMiPVrTB9EsZ6rAv+lWAeAWoE/MyLuBlaZ16IiYijFj5dtKG4isEhVk+VbmHWOuig+2xkUhwSaNK332i18fquWz6tR7PLWAszglTpBSinm3mq2t2qMG1A+r18+WrJ4xd9LARNSStPb+Bot6Vs+v95qq7ZpWo8fzqVd03osVT6PbaFde9ajpvIWeQ9S7A6+C/g3xa71mcBKFFv8NTuj1aqr/EHwDsWPiCZN633AXMpZfC7TtQAweKX8al0RaXL5fGpq+3m/k4H+EdGrRvgu0456mm4dtzzwZDvma6kmgKVSSlPa0X5wC9Pbsx4tOZwiGPdJKV1UOaHsLb5XK/MOpuqc54joUS6vcv2a1mPtlNIT81qwGpvHeKX5w4MUu403b8c8j1D8H96sxrQR7VjO/eXzF9vYfhbF7uLWltXW9Wjq5btl9YQy4GqtW3t9sny+usa0OV63DdM3p9hoqTxO39711gLM4JXmAymlccClFKepHB0Rc+yNiohVImLlilEXls8nRkSfinb9gZ/Tdn+i2Ho7OCK2qPG6K1SNegcY0sKyTqfoJXxqRKxaPbE877gynO4Fnge2iIivVDU/hE44vktxQ3io+jESEdtRnOLUmqMjol/FPH2Ak8rBCyvaXUix5+CYiJjjRvMRsVCtc6e1YHJXszT/OITifNfjgD3LjkVjgeUoOuWsD3wDGFW2vxz4OvBl4KmI+DtFJ6xdgYdoY2illMZHxB4U56reHhH/Ap6g6On8GYqQrQz8W4HdI+I6ig5SMyh6Jd+ZUnouIvalOAf56Yi4keKUnF4UnZo2B94GhpWvnSJiP4qrfF0dEU3n8a5NcUrWjcAX2vb2tehMih7gV0bE1RTHstcsl3sFxXvYkmfL9ag8j3cV4HrgkqZGKaV3ImJX4Frg/oi4FXiaYu/AUIrOVwMoTkfSAs7gleYTKaUpEbElcCDFaUO7UHxRjwX+B/yAIqCa2qeI2A34MbA3RXC/SbH1dRwwlTZKKV0fEevxcc/fz1Ocl/ocH2/hNWk6v3YbiotQLERxYY07y2X9OSIep7hQyFblst4H3qAI979WvfY95VbwiXy8u/sBii3U7ZjH4E0pPRERWwEnlPX2BB6nuLDFJFoP3q8BRwPfpPgB9DrFudAnp5SaHatPKd0aEZ8Bjizr3pzinOY3KC4EUmtXtxZAUfVvR5IkdSGP8UqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRgavJEkZGbySJGVk8EqSlJHBK0lSRv8fQJ7J46EFtucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(real, pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "plot_confusion_matrix(cnf_matrix, classes=np.unique(real), title=\"Confusion matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "jzYl5bRQwcMK",
    "outputId": "825835ff-ca3f-4bb0-9958-5c2a2ee02592"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6184668989547039"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### DUMMY CLASSIFIER ACCURACY (always nofault) ###\n",
    "\n",
    "sum(np.asarray(real) == 'nofault') / (sum(np.asarray(real) == 'fault') + sum(np.asarray(real) == 'nofault'))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Copia di rare_events.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow2]",
   "language": "python",
   "name": "conda-env-tensorflow2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
